Browsing by Author "Sheakh, Md. Alif"

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A Harmful Disorder: Predictive and Comparative Analysis for fetal Anemia Disease by Using Different Machine Learning Approaches
(IEEE, 2023) Hasan, Mahadi; Tahosin, Mst. Sazia; Farjana, Afia; Sheakh, Md. Alif; Hasan, Md Maruf
Anemia is a major issue for public health with significant implications for national development, it remains a largely neglected health problem in many developing countries. Iron deficiency is responsible for at least 50% of all cases of anemia and kills nearly 1 million people each year. Africa and Southeast Asia account for three quarters of these deaths. Surprisingly, one of the top ten risk factors that contributes to the global burden of disease is iron deficiency anemia (IDA). This study investigated the use of machine learning models to predict anemia. The study compared the performance of five different machine learning models: K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Gaussian Naive Bayes, and Light Gradient Boosting Machines. These models are combined using a voting classifier method to improve prediction accuracy. The study highlights the importance of accurately predicting diseases in the medical field. The ability to predict anemia at the right time is essential for effective prevention and treatment. This study demonstrates the potential of machine learning models to predict anemia and improve disease prevention and treatment. Using advanced algorithms and data processing techniques can help doctors make accurate predictions and make decisions, leading to better patient outcomes. This research result shows that the voting classifier achieved 99.95% accuracy.
Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images
(Scopus, 2024-09-16) Zannah, Tasnim Bill; Kafi, Md. Abdulla-Hil-; Shuva, Taslima Ferdaus; Bhuiyan, Touhid; Sheakh, Md. Alif; Hasan, Md. Zahid; Rahman, Md. Tanvir; Khan, Risala Tasin; Kaiser, M. Shamim; zaman, Md Whaiduz
Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.
Child and Maternal Mortality Risk Factor Analysis Using Machine Learning Approaches
(IEEE, 2023-05-26) Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hasan, Md Maruf; Islam, Taminul
Global attention is now being paid to maternal and child mortality. The incidence of maternal mortality is high in low and middle-income countries, particularly among adolescents and young adults. Healthcare professionals can monitor the mother's heartbeat during pregnancy to determine fetal viability using CTGs to prevent these deaths. To reduce child and maternal mortality, this work presented a risk factor analysis using machine learning approaches. As part of this study, this work evaluated seven machine learning algorithms. To assess the performance of different categorization algorithms, accuracy, precision, and recall were used. The random forest has achieved the highest 99.98% accuracy among the other algorithms. Initially, the dataset was imbalanced, after applying undersampling and oversampling methods, all algorithms performed excellently. A major focus of the present study was to predict the risk factor of child and maternal mortality using clinical data. Sending an ultrasound pulse and reading the response is how ultrasound devices work. To prevent child and maternal mortality, this analysis is an effective and cost-effective option for healthcare professionals.
Machine Learning Approach Analysis for Early-Stage Liver Disease Prediction in the Context of Bangladesh and India
(2024-02-29) Sheakh, Md. Alif; Taminul Islam; Sadik, Md. Rezwane; Rana, Md. Masum
Liver disease can cause death in millions of individuals worldwide. Early detection and accurate diagnosis improve patient outcomes and reduce mortality. Machine learning algorithms can detect and diagnose liver diseases more accurately and affordably. To test machine learning methods for early liver disease diagnosis, this study created a prediction model that distinguished those with liver illnesses from those without. Machine learning algorithms studied included Extra Tree, XGBoost, Random Forest, CatBoost, LogitBoost, Gradient Boosting, AdaBoost, and KNN. By using oversampling methodologies and assessing metrics like accuracy, recall, precision, and F1-score, the Extra Tree algorithm was found to be the most effective way for early liver problem detection with 99% accuracy. Undersampling, oversampling, and SMOTE reduced class imbalance. This problem is widespread in many machine learning applications. Machine learning algorithms may improve liver disease detection, early intervention, and healthcare costs. However, data protection, ethics, and healthcare professional training and understanding must be adequately addressed. This research is a major step toward a more accurate and practical liver disease diagnostic tool that could help individuals stay healthy and prevent liver illnesses.
Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI
(Scopus, 2024) Islam, Taminul; Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hena, Most. Hasna; Akash, Shopnil; Jardan, Yousef A. Bin; Wondmie, Gezahign Fentahun; Nafidi, Hiba-Allah; Bourhia, Mohammed
Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.
Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients by Use of Machine Learning Approach with Explainable Ai
(Springer Nature, 2024-04-11) Islam, Taminul; Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hena, Most. Hasna; Akash, Shopnil; Jardan, Yousef A. Bin; Wondmie, Gezahign Fentahun; Nafidi, Hiba‑Allah; Bourhia, Mohammed
Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.
Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI
(Scopus, 2024-04-11) Islam, Taminul; Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hena, Most. Hasna; Akash, Shopnil; Jardan, Yousef A. Bin; Wondmie, Gezahign Fentahun; Nafidi, Hiba-Allah; Bourhia, Mohammed
Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.