Browsing by Author "Afrin, Saima"

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Expert Cancer Model Using Supervised Algorithms with a Lasso Selection Approach
(International Journal of Electrical and Computer Engineering (IJECE), 2021) Ghosh, Pronab; Karim, Asif; Atik, Syeda Tanjila; Afrin, Saima; Saifuzzaman, Mohd.
One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
Expert Cancer Model Using Supervised Algorithms with a Lasso Selection Approach
(International Journal of Electrical and Computer Engineering (IJECE), Elsevier, 2021) Ghosh, Pronab; Karim, Asif; Atik, Syeda Tanjila; Afrin, Saima; Saifuzzaman, Mohd.
One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm
(2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), IEEE, 2020-11-18) Ghosh, Pronab; Shamrat, F. M. Javed Mehedi; Shultana, Shahana; Afrin, Saima; Anjum, Atqiya Abida; Khan, Aliza Ahmed
Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays. Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year. However, patients' lives can be saved with the fast detection of disease in the earliest stage. In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset. In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction. These algorithms are implemented on an online dataset of UCI machine learning repository. The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 99.80% accuracy. Later, different performance evaluation metrics have also been displayed to show appropriate outcomes. To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks.
SenseBOT: A Health Monitoring App to Collect Vital Life Saving Information Through IoT
(Daffodil International University, 2019-04) Morshed, Niaz; Afrin, Saima; Akid, Tanjid Ibna
“SenseBOT: A Health Monitoring App to Collect Vital Life Saving Information Through IoT”is a research based project that aims to create awareness about primary healthcare condition among the mass people of Bangladesh.This project is a combination of hardware and software. There is a device including 3 sensors which are LM35, Pulse sensor and BMP180. There isalsoa mobile application. The device collects three things. Pulse Rate, Body Temperature and Barometric Pressure. All the information is sent to the mobile application via bluetooth and is displayed in the screen. Thus, the user is able to know about his/her health condition in those sectors. There is a database where the health record of the user can be recorded for 30 days. This will reduce the accidental health failure rate in our country.
Supervised Machine Learning Based Liver Disease Prediction Approach with LASSO Feature Selection
(Bulletin of Electrical Engineering and Informatics, 2021) Afrin, Saima; Shamrat, F. M. Javed Mehedi; Nibir, Tafsirul Islam; Muntasim, Mst. Fahmida; Moharram, Md. Shakil; Imran, M. M.; Abdulla, Md
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.