setsci SETSCI Conference Proceedings 2687-5527 SET Technology 10.36287/setsci.22.48.001 f994bd0e-a2cd-4832-a05c-c243aab4f702 Kalp Hastalığı Risk Tahmini için Makine Öğrenmesi Tabanlı Mobil Uygulama Başaran Bartu Akdağ Ali Bilgisayar Mühendisliği Bölümü, Tokat Gaziosmanpaşa Üniversitesi 10 07 2025 22 75 81 © 2025 SET Technology 2025 SET Technology CC BY 4.0

Sunulan çalışmada, kalp hastalığı riskinin erken ve güvenilir biçimde tahmin edilmesini sağlayan makine öğrenmesi tabanlı bir mobil uygulama geliştirilmiştir. Dokuz farklı algoritma değerlendirilmiş, en yüksek doğruluğa (%86,81) sahip olan k-NN modeline rağmen, pratik uygulamalarda daha dengeli sonuçlar sunan Random Forest algoritması (%82,42 doğruluk) tercih edilmiştir. Uygulama, 13 sağlık parametresini (yaş, cinsiyet, kolesterol, kan basıncı vb.) kullanarak kalp hastalığı riskini tahmin eder. Sade ve erişilebilir arayüzü ile özellikle ileri yaştaki bireyler için optimize edilmiştir. Çalışma, bireysel farkındalığı artırmayı ve sağlık hizmetlerine yönelimi teşvik etmeyi amaçlamaktadır.

 Kalp hastalığı tespiti makine öğrenmesi mobil sağlık uygulamaları yapay zeka Heart disease detection machine learning mobile health applications artificial intelligence “The top 10 causes of death.” Accessed: Nov. 09, 2024. [Online]. Available: https://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death R. Luengo-Fernandez et al., “Cardiovascular disease burden due to productivity losses in European Society of Cardiology countries,” Eur Heart J Qual Care Clin Outcomes, vol. 10, no. 1, pp. 36–44, Jan. 2024, doi: 10.1093/EHJQCCO/QCAD031. M. Akman and S. Civek, “Dünyada ve Türkiye’de kardiyovasküler hastalıkların sıklığı ve riskin değerlendirilmesi,” The Journal of Turkish Family Physician, vol. 13, no. 1, pp. 21–28, Mar. 2022, doi: 10.15511/TJTFP.22.00121. “Dünyada ve Türkiye’de kardiyovasküler hastalıkların sıklığı ve riskin değerlendirilmesi - The Journal of Turkish Family Physician.” Accessed: Nov. 09, 2024. [Online]. Available: https://turkishfamilyphysician.com/makaleler/derleme/dunyada-ve-turkiyede-kardiyovaskuler-hastaliklarin-sikligi-ve-riskin-degerlendirilmesi/ “Kalp Hastalığı Risk Faktörleri: Medicover’dan Uzman Görüşleri.” Accessed: Nov. 09, 2024. [Online]. Available: https://www.medicoverhospitals.in/tr/articles/heart-disease-risk-factors-sangamner “Kardiyovasküler Hastalık: Belirtileri ve Önleme İpuçları.” Accessed: Nov. 09, 2024. [Online]. Available: https://www.medicoverhospitals.in/tr/articles/cardiovascular-disease-and-its-symptoms A. Ogunpola, F. Saeed, S. Basurra, A. M. Albarrak, and S. N. Qasem, “Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases,” Diagnostics, vol. 14, no. 2, Jan. 2024, doi: 10.3390/diagnostics14020144. N. A. Baghdadi, S. M. Farghaly Abdelaliem, A. Malki, I. Gad, A. Ewis, and E. Atlam, “Advanced machine learning techniques for cardiovascular disease early detection and diagnosis,” J Big Data, vol. 10, no. 1, Dec. 2023, doi: 10.1186/s40537-023-00817-1. K. Yetmezliği et al., “Issue (28),” Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences International Indexed and Refereed, vol. 165, no. 10, doi: 10.5281/zenodo.8238065. R. Detrano et al., “International Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease.” M. A. Khan et al., “Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN).,” Sci Rep, vol. 14, no. 1, p. 26241, Oct. 2024, doi: 10.1038/s41598-024-78021-1. T. Admassu, “Heart disease prediction model with k-nearest neighbor algorithm,” International Journal of Informatics and Communication Technology (IJ-ICT), vol. 10, p. 225, Jun. 2021, doi: 10.11591/ijict.v10i3.pp225-230. V. Shorewala, “Early detection of coronary heart disease using ensemble techniques,” Inform Med Unlocked, vol. 26, Jan. 2021, doi: 10.1016/j.imu.2021.100655. M. Pal and S. Parija, “Prediction of Heart Diseases using Random Forest,” J Phys Conf Ser, vol. 1817, p. 12009, Jun. 2021, doi: 10.1088/1742-6596/1817/1/012009. C. A. ul Hassan et al., “Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers,” Sensors, vol. 22, no. 19, Oct. 2022, doi: 10.3390/s22197227. Y. Rimal, N. Sharma, S. Paudel, A. Alsadoon, M. P. Koirala, and S. Gill, “Comparative analysis of heart disease prediction using logistic regression, SVM, KNN, and random forest with cross-validation for improved accuracy,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-93675-1. A. Alariyibi, M. El-Jarai, and A. Maatuk, “Evaluating the Accuracy of Classification Algorithms for Detecting Heart Disease Risk,” Machine Learning and Applications: An International Journal, vol. 10, no. 4, pp. 01–12, Dec. 2023, doi: 10.5121/mlaij.2023.10401. M. Shaheenur Islam Sumon et al., “CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data.” H. A. Al-Alshaikh et al., “Comprehensive evaluation and performance analysis of machine learning in heart disease prediction,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-58489-7. M. Abdullah, “Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron,” Front Artif Intell, vol. 7, 2024, doi: 10.3389/frai.2024.1539588. M. COŞAR and E. DENİZ, “Makine Öğrenimi Algoritmaları Kullanarak Kalp Hastalıklarının Tespit Edilmesi,” European Journal of Science and Technology, Oct. 2021, doi: 10.31590/ejosat.1012986. A. KÜÇÜKMANİSA and Z. H. KİLİMCİ, “Heart Disease Prediction with Machine Learning-Based Approaches,” Sakarya University Journal of Science, Nov. 2023, doi: 10.16984/saufenbilder.1312109. “Kalp Damar Sağlığı Bilgilendirme Portalı - Turkish Society of Cardiology.” Accessed: Nov. 09, 2024. [Online]. Available: https://tkd.org.tr/kardiyobil/kalp-damar-sagligi/kardiyovaskuler-risk-hesaplama “Kalp Hastalığı Riski Hesaplayıcı - Memorial.” Accessed: Nov. 09, 2024. [Online]. Available: https://www.memorial.com.tr/bilgi/kalp-hastaligi-risk-hesaplayici “Cardiovascular Risk Calculator App - PAHO/WHO | Pan American Health Organization.” Accessed: Nov. 09, 2024. [Online]. Available: https://www.paho.org/en/hearts-americas/cardiovascular-risk-calculator-app “ASCVD Risk Estimator +.” Accessed: Nov. 09, 2024. [Online]. Available: https://tools.acc.org/ASCVD-Risk-Estimator-Plus/#!/calculate/estimate/ “2018 Prevention Guidelines Tool CV Risk Calculator.” Accessed: Nov. 09, 2024. [Online]. Available: https://static.heart.org/riskcalc/app/index.html#!/baseline-risk “Heart Disease.” Accessed: Nov. 09, 2024. [Online]. Available: https://www.kaggle.com/datasets/data855/heart-disease/data M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten, “The WEKA data mining software: An update,” SIGKDD Explor. Newsl., vol. 11, pp. 10–18, May 2008. Y. Kaya and R. Tekin, “Comparison of discretization methods for classifier decision trees and decision rules on medical data sets,” Avrupa Bilim ve Teknoloji Dergisi, no. 35, pp. 275–281, 2022, doi: 10.31590/ejosat.1080098. I. Rish, “An Empirical Study of the Naïve Bayes Classifier,” IJCAI 2001 Work Empir Methods Artif Intell, vol. 3, May 2001. by J. Ross Quinlan, M. Kaufmann Publishers, and S. L. Salzberg, “Programs for Machine Learning,” 1994. L. Breiman, “Random Forests,” 2001. F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain [J],” Psychol. Review, vol. 65, pp. 386–408, May 1958, doi: 10.1037/h0042519. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans Inf Theory, vol. 13, no. 1, pp. 21–27, 1967, doi: 10.1109/TIT.1967.1053964. C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition.”