Browsing by Author "Rafi, Raisul Islam"

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A Predictive Analysis Framework of Heart Disease Using Machine Learning Approaches
(Daffodil International University, 22-06-29) Molla, Shourav; Shamrat, F. M. Javed Mehedi; Rafi, Raisul Islam; Umaima, Umme; Umaima, Umme; Hossain, Shahed; Mahmud, Imran
Heart disease is among the leading causes for death globally. Thus, early identification and treatment are indispensable to prevent the disease. In this work, we propose a framework based on machine learning algorithms to tackle such problems through the identification of risk variables associated to this disease. To ensure the success of our proposed model, influential data pre-processing and data transformation strategies are used to generate accurate data for the training model that utilizes the five most popular datasets (Hungarian, Stat log, Switzerland, Long Beach VA, and Cleveland) from UCI. The univariate feature selection technique is applied to identify essential features and during the training phase, classifiers, namely extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), gradient boosting (GB), and decision tree (DT), are deployed. Subsequently, various performance evaluations are measured to demonstrate accurate predictions using the introduced algorithms. The inclusion of Univariate results indicated that the DT classifier achieves a comparatively higher accuracy of around 97.75% than others. Thus, a machine learning approach is recognize, that can predict heart disease with high accuracy. Furthermore, the 10 attributes chosen are used to analyze the model's outcomes explain ability, indicating which attributes are more significant in the model's outcome.
Effective Heart Disease Prediction Using Machine Learning with Five Classifiers
(Daffodil International University, 23-02-18) Khan, MD. Tamim; Rafi, Raisul Islam
Heart disease is among the most difficult diseases, and it affects a lot of individuals all over the world. Early and accurate heart illness identification is vital in the world of medicine, particularly in the field of cardiology. Lower mortality rates could arise from the prevention or treatment of heart disease in the early stages. It takes more accuracy, perfection, and correctness to diagnose and predict heart-related disorders. Finding the greatest ML algorithm that can effectively predict cardiac disease is the system's major goal. We employed five hybrid classifiers, including the Decision Tree (DT), the Random Forest (RF), the Gradient Boosting Method (GBM), the Support Vector Machine (SVM), and the k-nearest neighbor algorithm (KNN). We have utilized the Univariate feature selection technique to choose key features. Moreover, we calculate F1 Score (F1), Precision (PRE), and Accuracy (ACC). The findings revealed that, when Univariate is taken into account, the RF classification algorithm achieves a comparably greater accuracy of roughly 98.31% than others. Thus, we discovered a relatively basic machine learning approach that may be utilized to create highly accurate heart disease prediction. In order to determine which attributes are more important in the model results, the chosen 08 features are also utilized to examine the model results for "interpretability".