An Effective Heart Disease Prediction Model Based on Machine Learning Techniques

Rony Chowdhury Ripan; Iqbal H. Sarker; Md Hasan Furhad; Md Musfique Anwar; Mohammed Moshiul Hoque

doi:10.1007/978-3-030-73050-5_28

An Effective Heart Disease Prediction Model Based on Machine Learning Techniques

Rony Chowdhury Ripan, Iqbal H. Sarker^*, Md Hasan Furhad, Md Musfique Anwar, Mohammed Moshiul Hoque

^*Corresponding author for this work

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

2 Scopus citations

Abstract

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

Original language	English
Title of host publication	Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020
Editors	Ajith Abraham, Thomas Hanne, Oscar Castillo, Niketa Gandhi, Tatiane Nogueira Rios, Tzung-Pei Hong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	280-288
Number of pages	9
ISBN (Print)	9783030730499
DOIs	https://doi.org/10.1007/978-3-030-73050-5_28
State	Published - 2021
Externally published	Yes

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1375 AIST
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Anomaly detection
Data analytics
Healthcare
Heart disease prediction
K-means clustering
Machine learning

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

UN SDGs

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

Access to Document

10.1007/978-3-030-73050-5_28

Cite this

Ripan, R. C., Sarker, I. H., Hasan Furhad, M., Musfique Anwar, M., & Hoque, M. M. (2021). An Effective Heart Disease Prediction Model Based on Machine Learning Techniques. In A. Abraham, T. Hanne, O. Castillo, N. Gandhi, T. Nogueira Rios, & T.-P. Hong (Eds.), Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020 (pp. 280-288). (Advances in Intelligent Systems and Computing; Vol. 1375 AIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73050-5_28

Ripan, Rony Chowdhury ; Sarker, Iqbal H. ; Hasan Furhad, Md et al. / An Effective Heart Disease Prediction Model Based on Machine Learning Techniques. Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020. editor / Ajith Abraham ; Thomas Hanne ; Oscar Castillo ; Niketa Gandhi ; Tatiane Nogueira Rios ; Tzung-Pei Hong. Springer Science and Business Media Deutschland GmbH, 2021. pp. 280-288 (Advances in Intelligent Systems and Computing).

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title = "An Effective Heart Disease Prediction Model Based on Machine Learning Techniques",

abstract = "This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.",

keywords = "Anomaly detection, Data analytics, Healthcare, Heart disease prediction, K-means clustering, Machine learning",

author = "Ripan, \{Rony Chowdhury\} and Sarker, \{Iqbal H.\} and \{Hasan Furhad\}, Md and Md Musfique Anwar and Hoque, \{Mohammed Moshiul\}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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doi = "10.1007/978-3-030-73050-5\_28",

language = "English",

isbn = "9783030730499",

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Ripan, RC, Sarker, IH, Hasan Furhad, M, Musfique Anwar, M & Hoque, MM 2021, An Effective Heart Disease Prediction Model Based on Machine Learning Techniques. in A Abraham, T Hanne, O Castillo, N Gandhi, T Nogueira Rios & T-P Hong (eds), Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020. Advances in Intelligent Systems and Computing, vol. 1375 AIST, Springer Science and Business Media Deutschland GmbH, pp. 280-288. https://doi.org/10.1007/978-3-030-73050-5_28

An Effective Heart Disease Prediction Model Based on Machine Learning Techniques. / Ripan, Rony Chowdhury; Sarker, Iqbal H.; Hasan Furhad, Md et al.
Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020. ed. / Ajith Abraham; Thomas Hanne; Oscar Castillo; Niketa Gandhi; Tatiane Nogueira Rios; Tzung-Pei Hong. Springer Science and Business Media Deutschland GmbH, 2021. p. 280-288 (Advances in Intelligent Systems and Computing; Vol. 1375 AIST).

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

TY - GEN

T1 - An Effective Heart Disease Prediction Model Based on Machine Learning Techniques

AU - Ripan, Rony Chowdhury

AU - Sarker, Iqbal H.

AU - Hasan Furhad, Md

AU - Musfique Anwar, Md

AU - Hoque, Mohammed Moshiul

PY - 2021

Y1 - 2021

N2 - This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

AB - This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

KW - Anomaly detection

KW - Data analytics

KW - Healthcare

KW - Heart disease prediction

KW - K-means clustering

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DO - 10.1007/978-3-030-73050-5_28

M3 - Conference contribution

AN - SCOPUS:85105941002

SN - 9783030730499

T3 - Advances in Intelligent Systems and Computing

SP - 280

EP - 288

BT - Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020

A2 - Abraham, Ajith

A2 - Hanne, Thomas

A2 - Castillo, Oscar

A2 - Gandhi, Niketa

A2 - Nogueira Rios, Tatiane

A2 - Hong, Tzung-Pei

PB - Springer Science and Business Media Deutschland GmbH

ER -

Ripan RC, Sarker IH, Hasan Furhad M, Musfique Anwar M, Hoque MM. An Effective Heart Disease Prediction Model Based on Machine Learning Techniques. In Abraham A, Hanne T, Castillo O, Gandhi N, Nogueira Rios T, Hong TP, editors, Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems, HIS 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 280-288. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-73050-5_28

An Effective Heart Disease Prediction Model Based on Machine Learning Techniques

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this