Advancements in Cardiovascular Disease Detection: Leveraging Data Mining and Machine Learning

Abstract

Cardiovascular disease (CVD) is a significant global health concern, requiring early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns and predicting CVD risk factors like blood pressure (BP), cholesterol levels, and genetic predispositions. Our research aims to predict CVD presence using ML algorithms, leveraging the Heart Disease UCI dataset with 14 attributes and 303 instances. Extensive feature engineering enhanced model performance. We developed five models using Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest Classifier, refining them with hyperparameter tuning. Results show substantial accuracy improvements post-tuning and feature engineering. Logistic Regression achieved the highest accuracy at 93.44%, closely followed by Support Vector Machine at 91.80%. Our findings emphasize the potential of ML in early CVD prediction, underlining its value in healthcare and proactive risk management. MLs utilization for CVD risk assessment promises personalized healthcare, benefiting both patients and healthcare providers. This research showcases the practicality and effectiveness of ML-based CVD risk assessment, enabling early intervention, improving patient outcomes, and optimizing healthcare resource allocation.