Analysis of Heart Disease Prediction Using Various Machine Learning Techniques
The health care industry produces a huge amount of data. These data are not always used to the full extent and are often underutilized. Using these huge amount of data, a disease can be detected, predicted, or even cured. Diseases like heart disease, cancer, tumor, and Alzheimer’s disease pose threat to mankind. In this paper, we try to concentrate on heart disease prediction. Using machine learning techniques, heart disease can be predicted.Medical data such as blood pressure, hypertension, diabetes, number of cigarettes smoked per day, and so on are taken as input and then these features are modeled for prediction. This model can then be used to predict future medical data. Algorithms like k-nearest neighbor, naïve Bayes, support vector machine, and decision tree are used. The accuracy of the model using each of the algorithms is calculated. Then the one with the good accuracy is taken as the model for predicting heart disease.