Abstract: Heart is the a huge part in living creatures. Analysis and detection of heart related illnesses requires more accuracy, flawlessness and rightness on the grounds that a little slip-up can cause exhaustion issue or demise of the individual, there are various demise cases identified with heart and their number is expanding dramatically.. Predicting of Heart disease illness saves many lives recognizing Symptoms namely Raising in the heartbeat, Slow heartbeat ,Chest pain or discomfort ,Shortness of breath ,Light headache., Dizziness and so forth, is a basic challenge by the customary clinical information investigation. In this paper , we analysed the Machine Learning algorithms like K-KNN, NB,Decision Tree And Random Forest .and proposed a hybrid model which can predict the heart disease based on the basic symptoms like age, sex, pulse Rate etc. by comparing the accuracy we proven hybrid algorithm is the most accurate and reliable algorithm compared to all algorithms.

Keywords: K-Nearest Algorithm,Logistic Algorithm,Naïve Bayes ,Multi-Layer Perceptron,Machine Learning Algorithms.

PDF | DOI: 10.17148/IJARCCE.2022.11112

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