Medikal Veri İşlemede Makine Öğrenme Yaklaşımları: Felç için Akıllı Teşhis Sistemi Önerisi

Yıl 2025, Cilt: 4 Sayı: 2, 446 - 459, 26.06.2025

Azra Şilan Peri , Nida Katı , Ferhat Uçar

https://doi.org/10.62520/fujece.1694558

Öz

Bu çalışma, felç teşhisi için makine öğrenmesi ve derin öğrenme tabanlı bir akıllı teşhis sistemi önermektedir. Sağlık sektöründe yapay zekânın (AI) kullanımı, büyük veri analitiği ve dijitalleşme ile birlikte artmaktadır. Felç, dünya genelinde yaygın bir nörolojik hastalık olup erken teşhisle ölüm ve sakatlık oranları önemli ölçüde azaltılabilir. Çalışmada, Kaggle platformundaki 4909 bireyi kapsayan “Felç Tahmin Veri Seti” kullanılmıştır. Bu veri seti, yaş, cinsiyet, hipertansiyon, kalp hastalığı, yaşam tarzı gibi 12 giriş özelliği ve felç durumunu gösteren bir çıkış özelliği içermektedir. Veri ön işleme adımları olarak eksik verilerin ortalama ile doldurulması, kategorik verilerin One-Hot Encoding ile sayısallaştırılması, Min-Max Ölçeklendirme ve SMOTE ile sınıf dengesizliği çözülmüştür. Çalışmada, 15 farklı makine öğrenmesi ve derin öğrenme algoritması (Random Forest, Voting Classifier, Histogram Gradient Boosting, SVM, MLP vb.) değerlendirilmiş; performansları doğruluk, hassasiyet, geri çağırma, F1-skoru ve ROC-AUC metrikleriyle ölçülmüştür. Voting Classifier, %98,5 doğruluk ve 0,99 AUC ile en yüksek performansı göstermiştir. Random Forest ve Histogram Gradient Boosting gibi ağaç tabanlı modeller de yüksek doğruluk oranlarıyla dikkat çekmiştir. Hiperparametre optimizasyonu için GridSearchCV ve RandomizedSearchCV kullanılmış, aşırı öğrenmeyi önlemek için erken durdurma, düzenlileştirme ve dropout teknikleri uygulanmıştır. Bulgular, topluluk öğrenme yöntemlerinin felç teşhisinde geleneksel yöntemlere üstünlük sağladığını göstermektedir. Çalışma, yapay zeka tabanlı klinik karar destek sistemlerinin sağlık sektörüne entegrasyonunun önemini vurgulamakta ve gelecekte daha büyük veri setleriyle model performansının artırılabileceğini önermektedir.

Anahtar Kelimeler

Felç teşhisi, Makine öğrenmesi, Derin öğrenme, Topluluk öğrenmesi, Klinik karar destek sistemleri

Etik Beyan

Hazırlanan makalede etik kurul onayına gerek yoktur. Ayrıca önerilen makalede herhangi bir kişi/kurumla çıkar çatışması yoktur.

Destekleyen Kurum

TÜBİTAK

Teşekkür

Araştırmamız, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 2209-A Üniversite Öğrencileri Araştırma Projeleri Destek Programı kapsamında, proje numarası 1919B012323732 ile finanse edilmiştir. Bu destek, projemizin yürütülmesine temel bir katkı sağlamıştır, tüm TÜBİTAK ekibine teşekkür ederiz.

Machine Learning Approaches in Medical Data Processing: A Proposal for an Intelligent Stroke Diagnosis System

Öz

This study proposes an intelligent diagnostic system based on machine learning and deep learning for stroke detection. The use of artificial intelligence (AI) in healthcare is increasing alongside big data analytics and digitalization. Stroke, a prevalent neurological disease worldwide, can have its mortality and disability rates significantly reduced through early diagnosis. The study utilizes the “Stroke Prediction Dataset” from Kaggle, encompassing 4909 individuals. This dataset includes 12 input features such as age, gender, hypertension, heart disease, and lifestyle factors, along with one output feature indicating stroke status. Data preprocessing steps involved filling missing values with the mean, converting categorical data to numerical format using One-Hot Encoding, applying Min-Max Scaling, and addressing class imbalance with SMOTE. Fifteen different machine learning and deep learning algorithms (e.g., Random Forest, Voting Classifier, Histogram Gradient Boosting, SVM, MLP) were evaluated, with performance measured using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The Voting Classifier achieved the highest performance with 98.5% accuracy and an AUC of 0.99. Tree-based models like Random Forest and Histogram Gradient Boosting also demonstrated high accuracy. Hyperparameter optimization was performed using GridSearchCV and RandomizedSearchCV, while early stopping, regularization, and dropout techniques were applied to prevent overfitting. The findings highlight the superiority of ensemble learning methods over traditional approaches in stroke diagnosis. The study underscores the importance of integrating AI-based clinical decision support systems into healthcare and suggests that model performance could be further enhanced with larger datasets in the future.

Anahtar Kelimeler

Stroke diagnosis, Machine learning, Deep learning, Ensemble learning, Clinical decision support systems

Etik Beyan

There is no need for an ethics committee approval in the prepared article. Also, there is no conflict of interest with any person/institution in the proposed article.

Destekleyen Kurum

TÜBİTAK

Teşekkür

Our research was funded by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under the 2209-A University Students Research Projects Support Program with project number 1919B012323732 This support provided a fundamental contribution to the execution of our project and is gratefully acknowledged.

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