Heart Disease Prediction Using Machine Learning Algorithms

Abstract: The heart is an important organ in living things. Heart-related disease diagnosis and prognosis calls for greater research precision, excellence, and correctness because even the smallest error can cause fatigue issues or individual death, there are many heart-related deaths are becoming more common, and their number is growing day, exponentially. A illness awareness prediction system is absolutely necessary to address the issue.Machine learning, a subset of artificial intelligence (AI), offers excellent assistance in making predictions about any form of event using data from real-world occurrences. In this study, utilising the UCI repository dataset for training and testing, we measure the accuracy of machine learning methods for predicting cardiac disease. These algorithms include k-nearest neighbour, decision tree, linear regression, and support vector machine (SVM). The greatest tool for implementing Python programming is the Anaconda (Jupytor) notebook, which has a variety of header files and libraries that improve the accuracy and precision of the task.

Keywords: supervised; unsupervised; reinforced; linear regression; decision tree; python programming; jupytor Notebook; confusion matrix;