Advanced Heart Disease Prediction Using Multiple Machine Learning Techniques-A Comprehensive Comparative Study
Advanced Heart Disease Prediction Using Multiple Machine Learning Techniques-A Comprehensive Comparative Study
Nalini Bodasingi1, P.Praveen Kumar2, G.Madhu Mohan Vamsi3, Cherukuri Gunalakshmi4, Simhadri Venkata Meena5
1Department of ECE, JNTUGVCEV, Viziangaram.
2Department of Information Technology, Gayatri Vidya Parishad College Of Engineering (Autonomous) ,Kommadi,Madhurawada,Visakhapatnam,Andhrapradesh.
3Department of CSE, Avanthi Institute of Engineering and Technology(A) ,Makavaripalem.
4Department of IECT, Mvgr College of Engineering(A), Vizianagaram.
5Department of CSE, Avanthi Institute of Engineering and Technology(A) ,Thagarapuvalasa.
ABSTRACT
Due to the fact that Heart Disease is one of the major causes of death worldwide, early and precise detection is essential for managing and treating the condition effectively. This study investigates the use of cutting-edge machine learning methods to forecast cardiac disease. Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbours, Naive Bayes, Neural Networks, and Voting Classifier are the ten models whose performances we assess. Our analysis reveals that ensemble methods and neural networks outperform traditional models, offering a robust framework for heart disease prediction. By leveraging large datasets and sophisticated algorithms, these models can identify complex patterns and interactions among risk factors. The results suggest that implementing these advanced techniques in clinical practice could enhance diagnostic accuracy, leading to better patient outcomes and more personalized treatment strategies. Future work could focus on refining these models and integrating them into healthcare systems to support decision-making and risk assessment.
Keywords: Heart disease prediction, Machine Learning, Disease Prediction, SVM, Naive Bayes.