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Prediction of Heart Disease by using Hiddend Markov Model and Fuzzy Classification


Jaybhaye Nagin Madhukar , KJ College of Engineering and Management Research,Pune; Kathile Shubhangi Jagannath, KJCOEMR,Pune; Ghorpade Vrushali Shamrao, KJCOEMR,Pune; Katake Rutuja Maruti, KJCOEMR,Pune


Fuzzy C Means Clustering, Hidden Morkov Models, Fuzzy Classification, Hear Disease Prediction


In today’s fast-paced world, a lot of individuals are not concentrating on their health as much and have increasing amounts of stress which lead to the introduction of a large number of unhealthy lifestyle choices that have been affecting their health significantly. These factors contribute to the large-scale increase in the number of heart diseases and failures which are highly difficult to predict. Even the doctors are unable to perform any predictions as they are unable to process the varied parameters that govern the rate of heart failures. Therefore, the machine learning paradigm comes to the rescue in this situation as it can analyze and process complex events to gain predictions. The machine learning approaches require massive amounts of data to enable complex processing, which is not ideally possible due to the inherent nature of personal medical data, due to which a sparse number of datasets are available in this regard. Therefore, this publication defines an innovative solution that utilizes Fuzzy C-means clustering, Logistic Regression along with Hidden Markov Model and Fuzzy Logic to achieve effective and accurate Heart Failure predictions. The proposed methodology has been rigorously tested against conventional approaches that have produced exceptional results and prove the increased accuracy and speed of the technique defined in this paper.

Other Details

Paper ID: IJSRDV8I70168
Published in: Volume : 8, Issue : 7
Publication Date: 01/10/2020
Page(s): 245-250

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