Efficient Machine Learning Model for Cardiac Disease Prediction

The most common disease affecting people lives are the disease related to the most vital part of the human body the heart. Identifying a cardiac disease is now becoming a very difficult task. As the society is advancing the primitive techniques are not capable enough to produce accurate result therefore machine learning a growing technology is being introduced in the sector which is aiding in reducing the death rate. Making computers more capable and improving their technicalities will make the model more efficient and accurate. Machine learning can solve this problem with the help of some prediction models. Some of the existing models like Decision tree, Support vector machine, K-Nearest Neighbor, Logistic Regression, and Naive Bayes Algorithm are tested and compared with the proposed model which proved to be more efficient and has better accuracy. Models were compared and tested under different parameters, out of which Logistic Regression, the proposed model came out with the best accuracy of 83.52%.