Early Detection of Coronary Heart Disease Based on Machine Learning Methods

Yıl 2022, Cilt: 4 Sayı: 1, 1 - 6, 01.01.2022

Rüstem Yılmaz Fatma Hilal Yağın

https://doi.org/10.37990/medr.1011924

Cited By: 18

Öz

Aim: Heart disease detection using machine learning methods has been an outstanding research topic as heart diseases continue to be a burden on healthcare systems around the world. Therefore, in this study, the performances of machine learning methods for predictive classification of coronary heart disease were compared.
Material and Method: In the study, three different models were created with Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms for the classification of coronary heart disease. For hyper parameter optimization, 3-repeats 10-fold repeated cross validation method was used. The performance of the models was evaluated based on Accuracy, F1 Score, Specificity, Sensitivity, Positive Predictive Value, Negative Predictive Value, and Confusion Matrix (Classification matrix).
Results: RF 0.929, SVM 0.897 and LR 0.861 classified coronary heart disease with accuracy. Specificity, Sensitivity, F1-score, Negative predictive and Positive predictive values of the RF model were calculated as 0.929, 0.928, 0.928, 0.929 and 0.928, respectively. The Sensitivity value of the SVM model was higher compared to the RF.
Conclusion: Considering the accurate classification rates of Coronary Heart disease, the RF model outperformed the SVM and LR models. Also, the RF model had the highest sensitivity value. We think that this result, which has a high sensitivity criterion in order to minimize overlooked heart patients, is clinically very important.

Anahtar Kelimeler

Heart Disease, machine learning, classification, random forest, parameter optimization

Kaynakça

• REFERENCES [1] T. Watanabe, K. Ando, H. Daidoji, Y. Otaki, S. Sugawara, M. Matsui, et al., "A randomized controlled trial of eicosapentaenoic acid in patients with coronary heart disease on statins," Journal of cardiology, vol. 70, pp. 537-544, 2017.
• [2] N. P. Paynter, R. Balasubramanian, F. Giulianini, D. D. Wang, L. F. Tinker, S. Gopal, et al., "Metabolic predictors of incident coronary heart disease in women," Circulation, vol. 137, pp. 841-853, 2018.
• [3] F. J. Wolters, R. A. Segufa, S. K. Darweesh, D. Bos, M. A. Ikram, B. Sabayan, et al., "Coronary heart disease, heart failure, and the risk of dementia: a systematic review and meta-analysis," Alzheimer's & Dementia, vol. 14, pp. 1493-1504, 2018.
• [4] M. V. Dogan, I. M. Grumbach, J. J. Michaelson, and R. A. Philibert, "Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study," PloS one, vol. 13, p. e0190549, 2018.
• [5] Ö. Ü. M. O. KAYA, "Performance Evaluation of Multilayer Perceptron Artificial Neural Network Model in the Classification of Heart Failure," The Journal of Cognitive Systems, vol. 6, pp. 35-38, 2021.
• [6] S. M. Birjandi and S. H. Khasteh, "A survey on data mining techniques used in medicine," Journal of Diabetes & Metabolic Disorders, pp. 1-17, 2021.
• [7] Z. T. KUCUKAKCALİ, İ. p. B. ÇİÇEK, E. GÜLDOĞAN, and C. ÇOLAK, "Assessment of Associative Classification Approach for Predicting Mortality by Heart Failure," The Journal of Cognitive Systems, vol. 5, pp. 41-45, 2020.
• [8] İ. p. B. ÇİÇEK, Z. KÜÇÜKAKÇALI, and C. ÇOLAK, "ASSOCIATIVE CLASSIFICATION APPROACH CAN PREDICT PROSTATE CANCER BASED ON THE EXTRACTED ASSOCIATION RULES," The Journal of Cognitive Systems, vol. 5, pp. 51-54.
• [9] İ. p. B. ÇİÇEK and Z. KÜÇÜKAKÇALI, "CLASSIFICATION OF HYPOTHYROID DISEASE WITH EXTREME LEARNING MACHINE MODEL," The Journal of Cognitive Systems, vol. 5, pp. 64-68.
• [10] Z. T. KÜÇÜKAKÇALI and İ. p. B. ÇİÇEK, "PERFORMANCE EVALUATION OF THE ENSEMBLE LEARNING MODELS IN THE CLASSIFICATION OF CHRONIC KIDNEY FAILURE," The Journal of Cognitive Systems, vol. 5, pp. 55-59.
• [11] A. K. ARSLAN, T. Zeynep, İ. p. B. ÇİÇEK, and C. ÇOLAK, "A NOVEL INTERPRETABLE WEB-BASED TOOL ON THE ASSOCIATIVE CLASSIFICATION METHODS: AN APPLICATION ON BREAST CANCER DATASET," The Journal of Cognitive Systems, vol. 5, pp. 33-40.
• [12] İ. p. B. ÇİÇEK and Z. KÜÇÜKAKÇALI, "Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network," Middle Black Sea Journal of Health Science, vol. 6, pp. 325-332, 2020.
• [13] Z. T. KÜÇÜKAKÇALI, İ. p. B. ÇİÇEK, and E. GÜLDOĞAN, "PERFORMANCE EVALUATION OF THE DEEP LEARNING MODELS IN THE CLASSIFICATION OF HEART ATTACK AND DETERMINATION OF RELATED FACTORS," The Journal of Cognitive Systems, vol. 5, pp. 99-103.
• [14] M. Siddhartha, "Heart Disease Dataset (Comprehensive)," Kaggle Inc, 2019.
• [15] K. Shah, H. Patel, D. Sanghvi, and M. Shah, "A comparative analysis of logistic regression, random forest and KNN models for the text classification," Augmented Human Research, vol. 5, pp. 1-16, 2020.
• [16] K. Kirasich, T. Smith, and B. Sadler, "Random forest vs logistic regression: binary classification for heterogeneous datasets," SMU Data Science Review, vol. 1, p. 9, 2018.
• [17] D. A. Pisner and D. M. Schnyer, "Support vector machine," in Machine Learning, ed: Elsevier, 2020, pp. 101-121.
• [18] P. Probst, M. N. Wright, and A. L. Boulesteix, "Hyperparameters and tuning strategies for random forest," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, p. e1301, 2019.
• [19] C. Iwendi, A. K. Bashir, A. Peshkar, R. Sujatha, J. M. Chatterjee, S. Pasupuleti, et al., "COVID-19 patient health prediction using boosted random forest algorithm," Frontiers in public health, vol. 8, p. 357, 2020.
• [20] D. Shah, S. Patel, and S. K. Bharti, "Heart disease prediction using machine learning techniques," SN Computer Science, vol. 1, pp. 1-6, 2020.
• [21] A. Kondababu, V. Siddhartha, B. B. Kumar, and B. Penumutchi, "A comparative study on machine learning based heart disease prediction," Materials Today: Proceedings, 2021.
• [22] B. Bahrami and M. H. Shirvani, "Prediction and diagnosis of heart disease by data mining techniques," Journal of Multidisciplinary Engineering Science and Technology (JMEST), vol. 2, pp. 164-168, 2015.
• [23] A. Ashari, I. Paryudi, and A. M. Tjoa, "Performance comparison between Naïve Bayes, decision tree and k-nearest neighbor in searching alternative design in an energy simulation tool," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 4, 2013.
• [24] H. Islam, Y. Elgendy, R. Segal, A. Bavry, and J. Bian, "Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach," Journal of Heart & Lung, pp. 1-7, 2017.