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A Hybrid Machine Learning Model for Scoring Risk of Myocardial Infarction
  • Subhagata Chattopadhyay
Subhagata Chattopadhyay
Formerly with GITAM Deemed to be University Dept. of Computer Science and Engineering Bangalore 561203 Karnataka India

Corresponding Author:[email protected]

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Abstract

A hybrid ML model is created for cardiac risk scoring using GMM clustering to group high-risk populations, Spearman correlation test, PCA, and Chi-square test are used for feature engineering. Significant feature sets are the input set to a feed-forward neural network (M:1;1) for cardiac risk scoring. The data used in this work is synthetic.