Machine Learning-Based Heart Disease Classification for Symptom-Driven Diagnostics

Muhammad Talha Jahangir ()
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Muhammad Talha Jahangir: Department of Computer Science, MNS-University of Engineering and Technology, Multan, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1768-1788

Abstract: Heart diseases are increasing over the period while identifying cardiac diseases at an early stage continuesto pose a challenge. This study focuses on the application of AI specifically in machine learning to improve early diagnosis of this ailment. We overcome the limitations of conventional diagnostic paradigms. Normalization was performed on a dataset with demographic and clinical characteristics data, outliers were removed, and principal components analysis was used to enhance and decrease dimensions to get optimized results. Supervised learning classifiers such as Support VectorMachine, Decision Trees, Random Forests, Logistic Regression, K-Nearest Neighbors, and Naive Bayes evaluated based on metrics such as confusion matrix, accuracy, and ROC AUC scores. Of all the models created, the Random Forest model was found to have the best internal validation results with an accuracy of 1.0 as well as test and training ROC AUCs of 0.97 for detecting heart disease cases and non-cases. It is evident that developing an AI model for the diagnosis of heart disease provides promising results of faster and more efficientdiagnosis reducing the mortality rates of the disease.

Keywords: Heart Disease; Machine Learning; Classification; Random Forest Classifier; K-Nearest Neighbor (KNN); Support Vector Machines (SVM); PCA (search for similar items in EconPapers)
Date: 2024
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