Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction with Reduced Number of Attributes using Principal Component Analysis | Zendy

Ayon Dey | Zendy; Jyoti Singh | Zendy; Neeta Singh | Zendy

Open Access

Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction with Reduced Number of Attributes using Principal Component Analysis

Author(s) -

Ayon Dey,

Jyoti Singh,

Neeta Singh

Publication year - 2016

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/ijca2016909231

Subject(s) - computer science , principal component analysis , machine learning , artificial intelligence , component (thermodynamics) , principal (computer security) , algorithm , pattern recognition (psychology) , operating system , physics , thermodynamics

research on causes of death due to heart disease has shown that it is the number one cause of death. If current trends are allowed to continue, 23.6 million people will die from heart disease in coming 2030. The healthcare industry gathers enormous amounts of heart disease data which unfortunately, are not "mined" to discover hidden information for effective decision making. In this paper, study of PCA has been done which finds the minimum number of attributes required to increase the accuracy of various supervised machine learning algorithms. The objective of this research is to analyze supervised machine learning algorithms to predict heart disease.

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