Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model

Dian Palupi Rini; Defrian Afandi; Desty Rodiah

doi:10.18495/comengapp.v11i3.415

[1]A. Adeliand M. Neshat,“A Fuzzy Expert System for Heart Disease Diagnosis,”inProceedings of the International MultiConference of Engineers and Computer Scientists (IMECS), 2010, I.134–139.
[2]D. Kurnianingtyas, W. F. Mahmudy, andA. W. Widodo,“Optimization Degree Membership from Fuzzy Tsukamoto Uses Genetic Algorithm for The Diagnosis of Beef Cow Disease,”Journal of Information Technology and Computer Science, Vol. 4, No. 1, 8–18, 2017.
[3]D. Pramasari,“Classification of Stages of Heart Disease Using the Fuzzy Hierarchical Model Method,” S.Kom. thesis,Sriwijaya University, Palembang,2019.
[4]A. Alharbi andF. Tchier, ”A Fuzzy-Genetic Algorithm Method for the Breast Cancer Diagnosis Problem,” inThe Ninth International Conference on Advanced Engineering Computing and Applications in Sciences(ADVCOMP), 2015, 122–127.
[5]A. A. A. Esmin andG. Lambert-Torres,”Evolutionary Computation Based Fuzzy Membership Functions Optimization,”in Conference Proceedings -IEEE International Conference on Systems, Man and Cybernetics, 2007, 823–828.
[6]J. Al Hafiz, ”Prediction of Stage Of Heart Disease With Fuzzy Method Sugeno Model Hierarchy And Certainty Factor,” S.Kom. thesis,SriwijayaUniversity, Palembang, 2020.
[7]S. Fadilah, “Fuzzy Tsukamoto's Membership Degree Optimization Uses Genetic Algorithms For Weather Prediction,” S.T. thesis,Sultan Syarif Kasim Islamic University, Riau, 2019.
[8]N. Z. Ilahiyah, D. E. Ratnawati, and C. Dewi,”Optimization of Limitations of Fuzzy Tsukamoto Membership Function Uses Genetic Algorithms For CreditWorthiness (Case Study: PD. BPR. Lamongan Regional Bank),” Journal of Information Technology Development and Computer Science,Vol. 2, No. 9, 2957–2964, 2018.
[9]M. K. Ardi, B. D.Setiawan, andM. A, Fauzi, “Tsukamoto's Fuzzy Inference System Optimization Uses Genetic Algorithms To Find Out The Length of Flush Time In Strawberry Plants,”Journal of Information Technology Development and Computer Science, Vol. 2, No. 10, 3802–3810, 2018.
[10]F. A. Fathurrachman, F. A. Bachtiar, and I.Cholissodin,“Optimization of Fuzzy Tsukamoto Membership Function with Genetic Algorithms on Gold Price Forecasting for Stock Trading,”Journal of Information Technology Development and Computer Science, Vol. 3, No. 4, 3939–3948, 2019.

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Affiliations

Dian Palupi Rini
Universitas Sriwijaya

Defrian Afandi
Affiliation not stated

Desty Rodiah
Affiliation not stated

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Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model

Dian Palupi Rini ,
Defrian Afandi ,
Desty Rodiah

Vol 11 No 3 (2022)

DOI 10.18495/comengapp.v11i3.415

Submitted: Jun 4, 2022

Published: Oct 1, 2022

Abstract

The Fuzzy Hierarchical Model method can be used to predict the stage of heart disease. The use of the Fuzzy Hierarchical Model on complex problems is still not optimal because it is difficult to find a fuzzy set that provides a more optimal solution. This method can be improved by changing the membership function constraints using Genetic Algorithm to get better predictions. Tests carried out using 282 heart disease patient data resulted in a Root Mean Squared Error (RMSE) value of 0.55 using the best Genetic Algorithm parameters, including population size of 140, number of generations of 125, and a combination of cross-over rate and mutation rate of 0.4 and 0.6 whereas the RMSE value generated by the Fuzzy Hierarchical Model before being optimized by the Genetic Algorithm was 0.89. These results indicate an increase in the predictive value of the Fuzzy Hierarchical Model after being optimized using the Genetic Algorithm.