Detection of Significant Risk Factors of Heart Diseases by Educing Multimodal Features and Implementing Machine Learning Techniques

One of the major reasons for deaths worldwide is heart diseases and possible detection at an earlier stage will prevent these attacks. Medical practitioners generate data with a wealth of concealed information present, and it’s not used effectively for predictions. For this reason, the research will convert the unused data into a dataset for shaping using different data mining techniques. People die having encountered symptoms that were not taken into considerations. The main objective of this paper is to analyze the most significant risk factors of Heart Diseases of patients by extracting multimodal features and predicting the occurrence of heart diseases using different classification techniques comparatively. This study will help improve the decision-making of medical professionals on the occurrence of heart diseases patients in a bid to enhance early detection by implementing comparatively several machine learning techniques resulting in an improved prediction accuracy using patient records (Multimodal Features).