Article: IHDPM: an integrated heart disease prediction model for heart disease prediction Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2022 Vol.14 No.6 pp.564 - 577 Abstract: The prediction of heart disease (HD) helps the physicians in taking accurate decisions towards the improvement of patient's health. Hence, machine learning (ML), data mining (DM), and classification techniques play a vital role in understanding and reducing the symptoms related to HDs. In this paper, an integrated heart disease prediction model (IHDPM) has been introduced for HD prediction by considering principal component analysis (PCA) for dimensionality reduction, sequential feature selection (SFS) for feature selection, and random forest (RF) classifier for classifications. Some experiments are performed by considering different evaluative measures on Cleveland Heart Disease Dataset (CHDD) sourced from the UCI-ML repository and Python language thereby concluding that the proposed model outperforms the other six conventional classification techniques. The proposed model will help out the physicians in conducting a diagnosis of the heart patients proficiently and at the same time, it can be applicable in predictions of other chronic diseases like diabetes, cancers, etc. Inderscience Publishers - linking academia, business and industry through research

Title: IHDPM: an integrated heart disease prediction model for heart disease prediction

Authors: Abhilash Pati; Manoranjan Parhi; Binod Kumar Pattanayak

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Abstract: The prediction of heart disease (HD) helps the physicians in taking accurate decisions towards the improvement of patient's health. Hence, machine learning (ML), data mining (DM), and classification techniques play a vital role in understanding and reducing the symptoms related to HDs. In this paper, an integrated heart disease prediction model (IHDPM) has been introduced for HD prediction by considering principal component analysis (PCA) for dimensionality reduction, sequential feature selection (SFS) for feature selection, and random forest (RF) classifier for classifications. Some experiments are performed by considering different evaluative measures on Cleveland Heart Disease Dataset (CHDD) sourced from the UCI-ML repository and Python language thereby concluding that the proposed model outperforms the other six conventional classification techniques. The proposed model will help out the physicians in conducting a diagnosis of the heart patients proficiently and at the same time, it can be applicable in predictions of other chronic diseases like diabetes, cancers, etc.

Keywords: machine learning; ML; data mining; DM; classification techniques; heart disease prediction.

DOI: 10.1504/IJMEI.2022.126526

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.6, pp.564 - 577

Received: 17 Oct 2020
Accepted: 09 Jan 2021
Published online: 28 Oct 2022 *

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Title: IHDPM: an integrated heart disease prediction model for heart disease prediction

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