Browsing by Author "Bui, Francis M."

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A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
(MDPI, 2023-06-18) Aziz, Md. Tarek; Mahmud, S. M. Hasan; Elahe, Md. Fazla; Jahan, Hosney; Rahman, Md Habibur; Nandi, Dip; Smirani, Lassaad K.; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis
(Scopus, 2024-09-28) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Ali, Md. Mamun; Ahmed, Kawsar; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U.
AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features
(Scopus, 2024-06-05) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Sultan, Md. Fahim; Ali, Md. Mamun; Ahmed, Kawsar; Hasan, Md. Zahid; Moustafa, Ahmed; Bui, Francis M.; Al-Zahrani, Fahad Ahmed
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach
Amp-rnnpro: A Two-stage Approach for Identification of Antimicrobials Using Probabilistic Features
(Springer Nature, 2024-06-05) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Sultan, Md. Fahim; Ali, Md. Mamun; Ahmed, Kawsar; Hasan, Md. Zahid; Moustafa, Ahmed; Bui, Francis M.; Al-Zahrani, Fahad Ahmed
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/.
An Intelligent Thyroid Diagnosis System Utilising Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes
(Elsevier, 2023-01-15) Sutradhar, Ananda; Al Rafi, Mustahsin; Ghosh, Pronab; Shamrat, F. M.Javed Mehedi; Moniruzzaman, Md.; Ahmed, Kawsar; Azad, AKM; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including Feature Importance (FIS), Information Gain Selections (IGS), and Least Absolute Shrinkage and Selection Operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as High-Risk Factors (HRF). Second, the models, including the Three Stage Hybrid Classifier (3SHC) and the Three Stage Hybrid Artificial Neural Network (3SHANN), are used as classifiers on the training data set. Third, a Local Interpretable Model-agnostic Explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a Partial Dependence Plot (PDP). Finally, the proposed system is validated with extensive experiments where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector.
DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model
(Elsevier, 2024-09-13) Rahman, Md. Ashikur; Ali, Md. Mamun; Ahmed, Kawsar; Mahmud, Imran; Bui, Francis M.; Chen, Li; Kumar, Santosh; Moni, Mohammad Ali
Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The identification of QSP is vital for understanding these biological processes. While existing clinical and lab-based methods are available, they can be costly and time-consuming. This study introduces Deep QSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. The DeepQSP model was evaluated using a dataset of 440 peptide sequences, achieving impressive performance metrics: 0.9697 accuracy, 0.9655 sensitivity, 0.9730 specificity, and a Matthews correlation coefficient (MCC) of 0.9385. The LSA combined with PAAC improves peptide sequence representation, while the CNN effectively captures complex patterns, leading to accurate QSP identification. These quantified results demonstrate the effectiveness of the Deep QSP method, offering a powerful tool for advancing the study of microbial interaction and quorum sensing. The enhanced identification of QSPs is critical for microbiology and bioengineering, aiding in the understanding of cell-to-cell communication in microorganisms.
Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier
(Tech Science Press, 2023-10-08) Mahmud, S M Hasan; Ali, Md Mamun; Shahriar, Mohammad Fahim; Al-Zahrani, Fahad Ahmed; Ahmed, Kawsar; Nandi, Dip; Bui, Francis M.
Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build a predictive model, open-source data was collected where five stages of images of AD were available as Cognitive Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and AD. Every stage of AD is considered as a class, and then the dataset was divided into three parts binary class, three class, and five class. In this research, we applied different preprocessing steps with augmentation techniques to efficiently identify AD. It integrates a random oversampling technique to handle the imbalance problem from target classes, mitigating the model overfitting and biases. Then three machine learning classifiers, such as random forest (RF), K-Nearest neighbor (KNN), and support vector machine (SVM), and two deep learning methods, such as convolutional neuronal network (CNN) and artificial neural network (ANN) were applied on these datasets. After analyzing the performance of the used models and the datasets, it is found that CNN with binary class outperformed 88.20% accuracy. The result of the study indicates that the model is highly potential to detect AD in the initial phase.
Development and Performance Analysis of Machine Learning Methods for Predicting Depression Among Menopausal Women
(Elsevier, 2023-05-25) Ali, Md. Mamun; Ali, Hussein; Algashamy, A.; Alzidi, Enas; Ahmed, Kawsar; Bui, Francis M.; Patel, Shobhit K.; Azam, Sami; Abdulrazak, Lway Faisal; Moni, Mohammad Ali
Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.
Discovering Common Pathophysiological Processes Between COVID-19 and Cystic Fibrosis by Differential Gene Expression Pattern Analysis
(Daffodil International University, 2022-04-29) Hasan, Md. Tanvir; Abdulrazak, Lway Faisal; Alam, Mohammad Khursheed; Islam, Md. Rezwan; Sathi, Yeasmin Hena; Al-Zahrani, Fahad Ahmed; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Coronaviruses are a family of viruses that infect mammals and birds. Coronaviruses cause infections of the respiratory system in humans, which can be minor or fatal. A comparative transcriptomic analysis has been performed to establish essential profiles of the gene expression of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) linked to cystic fibrosis (CF). Transcriptomic studies have been carried out in relation to SARS-CoV-2 since a number of people have been diagnosed with CF. The recognition of differentially expressed genes demonstrated 8 concordant genes shared between the SARS-CoV-2 and CF. Extensive gene ontology analysis and the discovery of pathway enrichment demonstrated SARS-CoV-2 response to CF. The gene ontological terms and pathway enrichment mechanisms derived from this research may affect the production of successful drugs, especially for the people with the following disorder. Identification of TF-miRNA association network reveals the interconnection between TF genes and miRNAs, which may be effective to reveal the other influenced disease that occurs for SARS-CoV-2 to CF. The enrichment of pathways reveals SARS-CoV-2-associated CF mostly engaged with the type of innate immune system, Toll-like receptor signaling pathway, pantothenate and CoA biosynthesis, allograft rejection, graft-versus-host disease, intestinal immune network for IgA production, mineral absorption, autoimmune thyroid disease, legionellosis, viral myocarditis, inflammatory bowel disease (IBD), etc. The drug compound identification demonstrates that the drug targets of IMIQUIMOD and raloxifene are the most significant with the significant hub DEGs.
Early Prediction of Chronic Kidney Disease
(Daffodil International University, 22-08-29) Mondol, Chaity; Shamrat, F. M. Javed Mehedi; Hasan, Md. Robiul; Alam, Saidul; Ghosh, Pronab; Tasnim, Zarrin; Ahmed, Kawsar; Bui, Francis M.; Ibrahim, Sobhy M.
Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD.
Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5
(Tech Science Press, 2023-01-01) Ontor, Md Zahid Hasan; Ali, Md Mamun; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed; Mahmud, S. M. Hasan; Azam, Sami
"Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries. For this many women have died. Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment. But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage. To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images. The system is designed using the YOLOv5 (You Only Look Once Version 5) model, which is a deep learning method. To build the model, cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed. Then the YOLOv5 models were applied to the labeled dataset to train the model. Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage. All of the model’s variations performed admirably. The model can effectively detect cervical cancerous cell, according to the findings of the experiments. In the medical field, our study will be quite useful. It can be a good option for radiologists and help them make the best selections possible."
Exploring the Therapeutic Targets of Stevioside in Management of Type 2 Diabetes by Network Pharmacology and In-silico Approach
(Elsevier, 2024-08-27) Dutta, Amit; Hossain, Md. Arju; Somadder, Pratul Dipta; Moli, Mahmuda Akter; Ahmed, Kawsar; Rahman, Md Masuder; Bui, Francis M.
Aims The main objective of the current study is to investigate the pathways and therapeutic targets linked to stevioside in the management of T2D using computational approaches. Methods We collected RNA-seq datasets from NCBI, then employed GREIN to retrieve differentially expressed genes (DEGs). Computer-assisted techniques DAVID, STRING and NetworkAnalyst were used to explore common significant pathways and therapeutic targets associated with T2D and stevioside. Molecular docking and dynamics simulations were conducted to validate the interaction between stevioside and therapeutic targets. Results Gene ontology and KEGG analysis revealed that prostaglandin synthesis, IL-17 signaling, inflammatory response, and interleukin signaling were potential pathways targeted by stevioside in T2D. Protein-protein interactions (PPI) analysis identified six common hub proteins (PPARG, PTGS2, CXCL8, CCL2, PTPRC, and EDN1). Molecular docking results showed best binding of stevioside to PPARG (−8 kcal/mol) and PTGS2 (−10.1 kcal/mol). Finally, 100 ns molecular dynamics demonstrated that the binding stability between stevioside and target protein (PPARG and PTGS2) falls within the acceptable range. Conclusions This study reveals that stevioside exhibits significant potential in controlling T2D by targeting key pathways and stably binding to PPARG and PTGS2. Further research is necessary to confirm and expand upon these significant computational results.
High-Precision Multiclass Classification of Lung Disease Through Customized MobileNetV2 From Chest X-Ray Images
(Elsevier, 2023-02-10) Shamrat, FM Javed Mehedi; Azam, Sami; Karim, Asif; Ahmed, Kawsar; Bui, Francis M.; Boer, Friso De
In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis. Initially, pre-processing is done on the X-ray images from the dataset using CLAHE to increase image contrast. Additionally, a Gaussian Filter, to denoise images, and data augmentation methods are used. The pre-processed images are fed into several transfer learning models; such as InceptionV3, AlexNet, DenseNet121, VGG19, and MobileNetV2. Among these models, MobileNetV2 performed with the highest accuracy of 91.6% in overall classifying lesions on Chest X-ray Images. This model is then fine-tuned to optimise the MobileLungNetV2 model. On the pre-processed data, the fine-tuned model, MobileLungNetV2, achieves an extraordinary classification accuracy of 96.97%. Using a confusion matrix for all the classes, it is determined that the model has an overall high precision, recall, and specificity scores of 96.71%, 96.83% and 99.78% respectively. The study employs the Grad-cam output to determine the heatmap of disease detection. The proposed model shows promising results in classifying multiple lesions on Chest X-ray images.
Highly Nonlinear Tellurite Photonic Crystal Fiber for Supercontinuum Generation
(Elsevier, 2023-09-29) Tahhan, Shaymaa R.; Mastin, Arkadiy; Yakasai, Izaddeen Kabir; Atieh, Ahmad; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed
This research study proposes a unique photonic crystal fiber (PCF) consisting of a solid core and a hexa-spiral shape, using tellurite as the background material. The proposed PCF exhibits numerous significant optical guiding properties, such as nonlinearity, dispersion, effective index, effective mode area, etc. Simulated findings from COMSOL Multiphysics v5.5 confirm that the PCF can achieve high nonlinearity and zero-dispersion at 1650 nm. The base material is tellurite, which has a significant nonlinear refractive index (RI) of 5.11 × 10−19 m2 W−1. The resultant wave has a bandwidth of 562.65 nm when pumped at a wavelength of 1650 nm. Additionally, a continuum can be produced by pumping at 1500 and 1550 nm. The designed PCF can generate a broad spectrum of supercontinuum with a pulse duration of 200 fs and 10 kW input power. Here is also performed measurements and inspections on the impact of higher-order Taylor series dispersion coefficients, pulse width, input power, etc. The proposed PCF has many potential applications, including nonlinear optics, high dispersion characteristics, infinitely single-mode fibers, high wattage beam transmission, particle trapping, and sensing. To address these applications, here is used COMSOL Multiphysics to generate data, such as propagation constants, attenuations, effective area, and fields, which were used by Optisystem. To the best of our knowledge, this is the first investigation to verify the performance of our designed PCF in Optisystem, for modeling signal transmission for supercontinuum generation and Raman shift.
Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach
(Daffodil International University, 2022-01-04) Islam, Md. Rakibul; Abdulrazak, Lway Faisal; Alam, Mohammad Khursheed; Paul, Bikash Kumar; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Background: Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods: A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣log fold change | >1 and P < 0.05. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results: Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.
Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
(MDPI Publications, 2023-10-29) Rahman, Md. Ashikur; Abdulrazak, Lway Faisal; Ali, Md. Mamun; Mahmud, Imran; Ahmed, Kawsar; Bui, Francis M.
Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.
Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques
(Hindawi Publications, 2023-05-02) Biswas, Niloy; Ali, Md Mamun; Rahaman, Md Abdur; Islam, Minhajul; Mia, Md. Rajib; Azam, Sami; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed; Moni, Mohammad Ali
Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time.
Machine Learning-based Statistical Analysis for Early Stage Detection of Cervical Cancer
(Computers in Biology and Medicine, Elsevier, 2021-12) Ali, Md Mamun; Ahmed, Kawsar; Bui, Francis M.; Paul, Bikash Kumar; Ibrahim, Sobhy M.; Quinn, Julian M.W.; Moni, Mohammad Ali
Cervical cancer (CC) is the most common type of cancer in women and remains a significant cause of mortality, particularly in less developed countries, although it can be effectively treated if detected at an early stage. This study aimed to find efficient machine-learning-based classifying models to detect early stage CC using clinical data. We obtained a Kaggle data repository CC dataset which contained four classes of attributes including biopsy, cytology, Hinselmann, and Schiller. This dataset was split into four categories based on these class attributes. Three feature transformation methods, including log, sine function, and Z-score were applied to these datasets. Several supervised machine learning algorithms were assessed for their performance in classification. A Random Tree (RT) algorithm provided the best classification accuracy for the biopsy (98.33%) and cytology (98.65%) data, whereas Random Forest (RF) and Instance-Based K-nearest neighbor (IBk) provided the best performance for Hinselmann (99.16%), and Schiller (98.58%) respectively. Among the feature transformation methods, logarithmic gave the best performance for biopsy datasets whereas sine function was superior for cytology. Both logarithmic and sine functions performed the best for the Hinselmann dataset, while Z-score was best for the Schiller dataset. Various Feature Selection Techniques (FST) methods were applied to the transformed datasets to identify and prioritize important risk factors. The outcomes of this study indicate that appropriate system design and tuning, machine learning methods and classification are able to detect CC accurately and efficiently in its early stages using clinical data.
Novel Approach of Anti-Resonant Fiber with Supporting 64 Orbital Angular Momentum Modes for Optical Communication
(Scopus, 22-03-29) Hassan, Md. Mehedi; Abdulrazak, Lway Faisal; Alharbi, Abdullah G.; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed; Uddin, Muhammad Shahin
A novel single material-based anti-resonant fiber is designed, investigated, and explored in this article with supports of up to 64 OAM modes over the 0.6 lm to 1.0 lm as operating wavelength. The other properties of the designed fiber including confinement loss (CL), OAM purity, effective refractive index differences (ERIDs), and dispersion variations provide the exceptional output. This fiber, the CL approximately varies between the 5.7031 105 dB/m to 9.2537 104 dB/m, the OAM purity is 97% to 99%, the ERIDs are higher than 104 for all the modes, and the least dispersion variations is 8.838 ps/km-nm for the EH1,1 mode. All the outstanding properties of the presented fiber are calculated using the FEM and the PML within the COMSOL Multiphysics simulator. Therefore, to the ideal of our knowledge, the proposed antiresonant fiber is mostly applicable in the high-quality long-distance fiber communications system.
PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors
(Springer Nature, 2023-06-11) Mia, Md. Rajib; Rahman, Md. Ashikur; Ali, Md. Mamun; Ahmed, Kawsar; Bui, Francis M.; Mahmud, S M Hasan
Chronic Kidney Disease (CKD) is major concern of death in recent years that can be cured by early treatment and proper supervision. But early detection of CKD and exact risk factors should be known to ensure proper treatment. The study mainly aims to address the issue by building a predictive model and discovers the most significant risk factors employing machine learning (ML) approach for CKD patients. Four individual machine learning classifiers were applied to conduct this study. It is found that GB performed very poor compare to other applied classifiers where RF and LightGBM outperformed with 99.167% accuracy. In terms of risk factors, it is found that sg, hemo, sc, pcv, al, rbcc, htn, dm, bgr, and sod are the most significant factors, which are mainly correlated with CKD. The study and its findings indicate that it will enable patients, doctors and clinicians to identify CKD patients early and ensure proper treatment for them.