In today's world heart disease becomes the threatening disease. The life style changes, environmental changes, usages of plastic increases the heart disease patients throughout the world. Machine Learning (ML) algorithms produce good results in predicting and making decisions for health care problems. ML gives solutions not only to health care related problems but also for Internet of Things (IoT) related problems. Many researches works deals with heart disease predictions with ML techniques. In the proposed approach, various machine learning techniques are applied such as K-Nearest Neighbor, Naïve Bayes and Random Forest for classifying heart disease in a given dataset. Finally the model is implemented with combined features and applied known classification techniques for heart disease prediction. Among all the techniques, combined feature model with Random Forest technique produces the accuracy of 81%.


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    Title :

    Identification of Heart Disease Using Machine Learning Approach


    Contributors:


    Publication date :

    2021-12-02


    Size :

    2532691 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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