An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection

Samina Kanwal; Junaid Rashid; Muhammad Wasif Nisar; Jungeun Kim; Amir Hussain

doi:10.1109/MAJICC53071.2021.9526242

An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection

Samina Kanwal
, Junaid Rashid
, Muhammad Wasif Nisar
, Jungeun Kim
, Amir Hussain

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Scopus citations

Abstract

Heart disease is the world's leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genetic algorithm (GA) to select attributes, which are used as input for the machine learning algorithms Deep Learning(DL), Support Vector Machine (SVM), Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The two datasets of heart disease are used for model implementation. The results evaluation is measured using accuracy, precision, and f-measure. The proposed model achieves the 92% result in the term of accuracy. In terms of precision, 96% result is achieved.

Original language	English
Title of host publication	Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665424134
DOIs	https://doi.org/10.1109/MAJICC53071.2021.9526242
State	Published - 15 Jul 2021
Externally published	Yes
Event	1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021 - Karachi, Pakistan Duration: 15 Jul 2021 → 17 Jul 2021

Publication series

Name	Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

Conference

Conference	1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021
Country/Territory	Pakistan
City	Karachi
Period	15/07/21 → 17/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

features
genetic
heart disease
machine learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/MAJICC53071.2021.9526242

Cite this

Kanwal, S., Rashid, J., Nisar, M. W., Kim, J., & Hussain, A. (2021). An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection. In Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021 (Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MAJICC53071.2021.9526242

Kanwal, Samina ; Rashid, Junaid ; Nisar, Muhammad Wasif et al. / An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection. Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021).

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title = "An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection",

abstract = "Heart disease is the world's leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genetic algorithm (GA) to select attributes, which are used as input for the machine learning algorithms Deep Learning(DL), Support Vector Machine (SVM), Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The two datasets of heart disease are used for model implementation. The results evaluation is measured using accuracy, precision, and f-measure. The proposed model achieves the 92\% result in the term of accuracy. In terms of precision, 96\% result is achieved.",

keywords = "features, genetic, heart disease, machine learning",

author = "Samina Kanwal and Junaid Rashid and Nisar, \{Muhammad Wasif\} and Jungeun Kim and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021 ; Conference date: 15-07-2021 Through 17-07-2021",

year = "2021",

month = jul,

day = "15",

doi = "10.1109/MAJICC53071.2021.9526242",

language = "English",

series = "Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021",

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Kanwal, S, Rashid, J, Nisar, MW, Kim, J & Hussain, A 2021, An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection. in Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021. Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021, Institute of Electrical and Electronics Engineers Inc., 1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021, Karachi, Pakistan, 15/07/21. https://doi.org/10.1109/MAJICC53071.2021.9526242

An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection. / Kanwal, Samina; Rashid, Junaid; Nisar, Muhammad Wasif et al.
Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Rashid, Junaid

AU - Nisar, Muhammad Wasif

AU - Kim, Jungeun

AU - Hussain, Amir

PY - 2021/7/15

Y1 - 2021/7/15

N2 - Heart disease is the world's leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genetic algorithm (GA) to select attributes, which are used as input for the machine learning algorithms Deep Learning(DL), Support Vector Machine (SVM), Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The two datasets of heart disease are used for model implementation. The results evaluation is measured using accuracy, precision, and f-measure. The proposed model achieves the 92% result in the term of accuracy. In terms of precision, 96% result is achieved.

AB - Heart disease is the world's leading cause of increasing death rates. Although there is a lot of research in the medical sector, an efficient and reliable model to predict this disease at an early stage is still required. So, early diagnosis of heart disease is the most promising strategy for effective treatment. In this paper, we utilize the Genetic algorithm (GA) to select attributes, which are used as input for the machine learning algorithms Deep Learning(DL), Support Vector Machine (SVM), Neural network (NN), Naive Bayes (NB), and Logistic regression (LR). The two datasets of heart disease are used for model implementation. The results evaluation is measured using accuracy, precision, and f-measure. The proposed model achieves the 92% result in the term of accuracy. In terms of precision, 96% result is achieved.

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M3 - Conference contribution

AN - SCOPUS:85115691567

T3 - Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

BT - Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

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Kanwal S, Rashid J, Nisar MW, Kim J, Hussain A. An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection. In Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021). doi: 10.1109/MAJICC53071.2021.9526242

An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this