Heart Disease Prediction Using Machine Learning Techniques

Through this research we have attempted to analyze the various machine learning techniques and anticipate if someone in particular, given different individual attributes and indications, will get coronary illness or not. The primary thought process of our report was to looking at the exactness and analyzing the reasons behind the variation of different algorithms. We have used Cleveland dataset for heart diseases which contains 1025 instances and used percent split to divide the data into two sections which are training and testing datasets. We have considered 14 attributes and implemented four different algorithms to examine the accuracy. By the end of the implementation part, we have discovered that Random Forest is giving the maximum accuracy level in our dataset which is 99 percent and Decision Tree is playing out the leastwith an accuracy level of 85 percent. Probably for other instances and different datasets other algorithm may work in better manner however for our situation, we have discovered this outcome. Also, on the off chance that we increment the number of training data, maybe we can find more accurate result but it will take more time to process and the system will be slower than now as it will be more perplexing and will be handling more data.

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