Machine Learning-Based Heart Disease Classification for Symptom-Driven Diagnostics
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
Heart diseases are increasing over the period while identifying cardiac diseases at an early stage continues 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 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 Vector Machine, 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 noncases. It is evident that developing an AI model for the diagnosis of heart disease provides promising results of faster and more efficient diagnosis reducing the mortality rates of the disease.
Authors and Affiliations
Muhammad Talha Jahangir, Tahir Abbas, Muhammad Hamza Khan, Amjad Ali, Burhan Mughees, Afaq Ahmad, Muhammad Ahsan Jamil
Keywords
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- EP ID EP760552
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