Heart Diseases Prediction using Machine Learning
Anup Lal Yadav, Kamal Soni, Shanu Khare
Abstract
The medical field relies heavily on data analysis to accurately diagnose diseases. To achieve this, various research methods must be filtered and appropriate equipment used based on the severity of the pathology. Artificial intelligence offers a distinct classification of software that can effectively analyze and present data for the best possible predictions.Our system employs a basic model that can perform various data processing algorithms to analyze different heart diseases. The model is specifically designed to work with a particular category of data. To obtain a prediction model, we train the system using available data and then test it using new data. We utilize different optimization techniques to classify current data and improve prediction accuracy.Our main goal is to create a framework for predicting different heart diseases using various algorithms, such as decision tree, random forest, logistic regression, and KNN. To achieve this, we conduct research using the heart disease dataset provided by the UCI Machine Repository. Additionally, our system provides a user-friendly interface to facilitate ease of use.