Implementation of Disease Detection and Prediction of Heart using Machine Learning

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Heart disease detection and prediction is an important task for entrusting whether the person is healthy or not. In recent years, there has been an increasing interest in the use of feature extraction methods for this purpose. Feature extraction methods are techniques used to identify relevant information from images and other forms of data. This information can then be used to train machine learning models to classify and predict diseases. Various feature extraction methods have been proposed for heart disease detection and prediction, including color-based features, texture-based features, and shape-based features. Color based features involve analyzing the color of blood vessels in angiography. Texture-based features involve analyzing the texture patterns within medical images, including those related to heart disease and helps to identify the texture of heart tissue in an MRI scan. Shape-based features can be used to analyze the shapes and outline of structures within medical images. Predicting heart disease using machine learning involves developing a system that can analyze various health-related data to determine the likelihood of a person having heart disease.