Version 1
: Received: 27 November 2020 / Approved: 30 November 2020 / Online: 30 November 2020 (16:38:23 CET)
How to cite:
Ripan, R. C.; Sarker, I. H.; Furhad, M. H.; Anwar, M. M.; Hoque, M. M. An Effective Heart Disease Prediction Model based on Machine Learning Techniques. Preprints2020, 2020110744. https://doi.org/10.20944/preprints202011.0744.v1
Ripan, R. C.; Sarker, I. H.; Furhad, M. H.; Anwar, M. M.; Hoque, M. M. An Effective Heart Disease Prediction Model based on Machine Learning Techniques. Preprints 2020, 2020110744. https://doi.org/10.20944/preprints202011.0744.v1
Ripan, R. C.; Sarker, I. H.; Furhad, M. H.; Anwar, M. M.; Hoque, M. M. An Effective Heart Disease Prediction Model based on Machine Learning Techniques. Preprints2020, 2020110744. https://doi.org/10.20944/preprints202011.0744.v1
APA Style
Ripan, R. C., Sarker, I. H., Furhad, M. H., Anwar, M. M., & Hoque, M. M. (2020). An Effective Heart Disease Prediction Model based on Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints202011.0744.v1
Chicago/Turabian Style
Ripan, R. C., Md Musfique Anwar and Mohammed Moshiul Hoque. 2020 "An Effective Heart Disease Prediction Model based on Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints202011.0744.v1
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 \textit{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.
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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