Effective Heart Disease Prediction using Hybr...

Effective Heart Disease Prediction using Hybrid Machine Learning Techniques

Author Name : P. Ajay Kumar, K Rakesh, K Hemanth Kumar, S Amrutha Varshini, Mr. K L Ganapathi Reddy, Dr. Akula Siva Prasad

DOI: https://doi.org/10.56025/IJARESM.2023.1201244270

ABSTRACT The project titled "Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques" aims to develop a robust and accurate predictive model for identifying individuals at risk of heart disease. Recognizing the complexity of factors contributing to heart disease, the project employs a hybrid approach that integrates various machine learning techniques to enhance prediction performance. The proposed model incorporates diverse data sources, including demographic information, medical history, lifestyle factors, and physiological indicators. By leveraging the strengths of different machine learning algorithms such as decision trees, support vector machines, neural networks, and ensemble methods, the hybrid model seeks to capture intricate patterns and relationships within the data, thereby improving the overall predictive capability. The project emphasizes continuous refinement and updates to ensure the model remains adaptable to evolving healthcare landscapes and changing patient profiles. Through the integration of both traditional statistical methods and advanced machine learning algorithms, the project aspires to provide an effective and reliable tool for early heart disease prediction. The outcomes of this research hold significant potential for improving preventive healthcare strategies and ultimately reducing the burden of heart disease in the population.