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

Muhammad Talha Jahangir; Muhammad Hamza Khan; Amjad Ali; Burhan Mughees; Afaq Ahmad; Muhammad Ahsan Jamil

Authors

Muhammad Talha Jahangir Department of Computer Science, MNS-University of Engineering and Technology, Multan, Pakistan
Muhammad Hamza Khan Department of Electrical Engineering, MNS UET, Multan, Pakistan
Amjad Ali Department of Information Technology, Bahauddin Zakariya University, Multan, Pakistan
Burhan Mughees FAST - National University of Computer and Emerging Sciences, Faisalabad Campus
Afaq Ahmad Punjab Tianjin University of Technology, Lahore, Pakistan
Muhammad Ahsan Jamil Institute of Computing, MNS-University of Agriculture, Multan

Keywords:

Heart Disease, Machine Learning, Classification, Random Forest Classifier, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), PCA.

Abstract

Heart diseases are increasing over the period, while identifying cardiac diseases at an early stage continue to pose a challenge. This study focuses on the application of AI specifically in machine learning to improve early diagnosis of this ailment. We overcome 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. The followed classifiers were used: Decision Trees, Random Forests, Logistic Regression, K- Nearest Neighbors, and Naive Bayes, SVM with an assessment of the models based on the 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 efficient diagnosis reducing the mortality rates of the disease.

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