This study presents the development of a mobile application that enables early and reliable prediction of heart disease risk using machine learning techniques. Nine different algorithms were evaluated, and although the k-Nearest Neighbors (k-NN) model achieved the highest accuracy (86.81%), the Random Forest algorithm was preferred due to its more balanced performance in practical scenarios (accuracy: 82.42%). The application estimates heart disease risk based on 13 health parameters, including age, gender, cholesterol level, and blood pressure. With its simple and accessible interface, the app is optimized particularly for older adults. The primary aim of the study is to raise individual awareness and encourage timely engagement with healthcare services.

Keywords - Heart disease detection, machine learning, mobile health applications, artificial intelligence

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.

Keywords^Turkish - Kalp hastalığı tespiti, makine öğrenmesi, mobil sağlık uygulamaları, yapay zeka

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