A COMPARATIVE STUDY FOR THE PREDICTION OF HEART ATTACK RISK AND ASSOCIATED FACTORS USING MLP AND RBF NEURAL NETWORKS

Year 2021, Volume: 6 Issue: 2, 51 - 54, 30.12.2021

Rüstem Yılmaz , Fatma Hilal Yağın

https://doi.org/10.52876/jcs.1001680

Cited By: 3

Abstract

Abstract— Aim: The aim of this study is to develop a predictive classification model that can identify risk factors for heart attack disease.
Materials and Methods: In the study, patients with low and high probability of having a heart attack were examined. Variable importance was calculated to identify risk factors. The radial basis function and multilayer perception neural networks were used to compare the classification prediction results.
Results: MLP model criteria; Accuracy 0.911, F1 score 0.918, Specificity 0.92, Sensitivity 0.903, while RBF model criteria were obtained as accuracy 0.797, F1 score 0.812, Specificity 0.84, Sensitivity 0.765. The first three most important factors that may be associated with having a heart attack were obtained as trestbps, oldpeak, and chol.
Conclusion: According to the prediction results of the heart attack, it can be said that the model created with the MLP neural network has more successful predictions than the model created with the RBF neural network. In addition, estimating the importance values of the factors most associated with heart attack (obtaining the most important biomarkers that may cause heart attack) is a promising result for the diagnosis, treatment and prognosis of the disease.

Keywords— Heart Attack, machine learning, neural networks, classification, variable importance.

Keywords

Heart Attack, machine learning, neural networks, classification, variable importance

References

• 1. Brice JH, Griswell JK, Delbridge TR, Key CB. S TROKE: F ROM R ECOGNITION BY THE P UBLIC TO M ANAGEMENT BY E MERGENCY M EDICAL S ERVICES. Prehospital Emergency Care. 2002;6(1):99-106.
• 2. Park MH, Jo SA, Jo I, Kim E, Eun S-Y, Han C, et al. No difference in stroke knowledge between Korean adherents to traditional and western medicine–the AGE study: an epidemiological study. BMC Public Health. 2006;6(1):1-9.
• 3. KUCUKAKCALİ ZT, ÇİÇEK İpB, GÜLDOĞAN E, ÇOLAK C. Assessment of Associative Classification Approach for Predicting Mortality by Heart Failure. The Journal of Cognitive Systems. 2020;5(2):41-5.
• 4. KÜÇÜKAKÇALI ZT, ÇİÇEK İpB, GÜLDOĞAN E. PERFORMANCE EVALUATION OF THE DEEP LEARNING MODELS IN THE CLASSIFICATION OF HEART ATTACK AND DETERMINATION OF RELATED FACTORS. The Journal of Cognitive Systems.5(2):99-103.
• 5. ÇİÇEK İpB, KÜÇÜKAKÇALI Z. Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network. Middle Black Sea Journal of Health Science. 2020;6(3):325-32.
• 6. ÇİÇEK İpB, KÜÇÜKAKÇALI Z, ÇOLAK C. ASSOCIATIVE CLASSIFICATION APPROACH CAN PREDICT PROSTATE CANCER BASED ON THE EXTRACTED ASSOCIATION RULES. The Journal of Cognitive Systems.5(2):51-4.
• 7. KÜÇÜKAKÇALI ZT, ÇİÇEK İpB. PERFORMANCE EVALUATION OF THE ENSEMBLE LEARNING MODELS IN THE CLASSIFICATION OF CHRONIC KIDNEY FAILURE. The Journal of Cognitive Systems.5(2):55-9.
• 8. PERÇİN İ, YAĞIN FH, ARSLAN AK, ÇOLAK C, editors. An Interactive Web Tool for Classification Problems Based on Machine Learning Algorithms Using Java Programming Language: Data Classification Software. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); 2019: IEEE.
• 9. YAĞIN FH, GÜLDOĞAN E, UCUZAL H, ÇOLAK C. A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images. Konuralp Medical Journal.13(S1):438-45.
• 10. Yegnanarayana B. Artificial neural networks: PHI Learning Pvt. Ltd.; 2009.
• 11. Orr MJ. Introduction to radial basis function networks. Technical Report, center for cognitive science, University of Edinburgh …; 1996.
• 12. KARAMAN U, ÇİÇEK İpB. DETERMINATION OF CRYPTOSPORIDIUM SPP. RISK FACTORS USING MULTILAYER PERCEPTRON NEURAL NETWORK AND RADIAL BASED FUNCTIONAL ARTIFICIAL NEURAL NETWORK METHOD. The Journal of Cognitive Systems.5(2):83-7.
• 13. Després J-P, Lamarche B, Mauriège P, Cantin B, Dagenais GR, Moorjani S, et al. Hyperinsulinemia as an independent risk factor for ischemic heart disease. New England Journal of Medicine. 1996;334(15):952-8.
• 14. McNeer JF, Margolis JR, Lee KL, Kisslo JA, Peter RH, Kong Y, et al. The role of the exercise test in the evaluation of patients for ischemic heart disease. Circulation. 1978;57(1):64-70.
• 15. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
• 16. Sidey-Gibbons JA, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC medical research methodology. 2019;19(1):1-18.
• 17. Perçın İ, Yağin FH, Güldoğan E, Yoloğlu S, editors. ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP); 2019: IEEE.
• 18. Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European heart journal. 2019;40(24):1975-86.