Büyük Verilere Dayalı Kardiyovasküler Hastalık Risklerinin Tahmini İçin Veri Dengeleme ile Farklı Makine Öğrenmesi Modellerinin Karşılaştırılması

Abstract

Kardiyovasküler hastalıklar (KVH), önde gelen küresel ölüm ve morbidite nedenidir. Bu çalışma, büyük veri kullanarak KVH riskini tahmin etmek için veri dengeleme tekniklerini (SMOTE, ENN, SMOTE-ENN, SMOTE-Tomek) ve makine öğrenimi (ML) algoritmalarını değerlendirir. 308.854 kayıt içeren 2021 CDC BRFSS veri kümesi, eksik ve alakasız veriler kaldırılarak ön işleme tabi tutuldu. Veri kümesi, %80 eğitim ve %20 test alt kümelerine ayrıldı. Lojistik regresyon, rastgele orman, LightGBM, XGBoost ve CatBoost dahil olmak üzere ML modelleri, dengeli veriler üzerinde eğitildi. Doğruluk, kesinlik, geri çağırma, F1 puanı, ROC eğrisi ve AUC gibi performans ölçütleri değerlendirme için kullanıldı. SMOTE-ENN ve SMOTE-Tomek model performansını iyileştirdi, LightGBM ve CatBoost en yüksek AUC ve F1 puanlarına ulaştı. Sonuçlar, özellikle SMOTE-ENN olmak üzere veri dengelemenin model duyarlılığını artırarak kardiyovasküler hastalık riskinin belirlenmesine yardımcı olduğunu göstermektedir. Bu bulgular, halk sağlığı hemşireliğinde makine öğreniminin hedefli müdahaleler geliştirme ve sonuçları iyileştirme potansiyelinin altını çizmektedir.

Keywords

• kardiyovasküler hastalıklar
• makine öğrenimi
• büyük veri
• risk değerlendirmesi
• hemşirelik liderliği

Comparison of Different Machine Learning Models with Data Balancing for Prediction of Cardiovascular Disease Risks Based on Big Data

Abstract

Cardiovascular diseases (CVD) are a leading global cause of death and morbidity. This study evaluates data balancing techniques (SMOTE, ENN, SMOTE-ENN, SMOTE-Tomek) and machine learning (ML) algorithms for predicting CVD risk using big data. The 2021 CDC BRFSS dataset, with 308,854 records, was preprocessed by removing missing and irrelevant data. The dataset was split into 80% training and 20% testing subsets. ML models, including logistic regression, random forest, LightGBM, XGBoost, and CatBoost, were trained on balanced data. Performance metrics such as accuracy, precision, recall, F1 score, ROC curve, and AUC were used for evaluation. SMOTE-ENN and SMOTE-Tomek improved model performance, with LightGBM and CatBoost achieving the highest AUC and F1 scores. Results demonstrate that data balancing, especially SMOTE-ENN, enhances model sensitivity, aiding CVD risk identification. These findings underscore the potential for ML in nursing to develop targeted interventions and improve outcomes.

Keywords

• cardiovascular diseases
• machine learning
• big data
• risk assessment
• nursing leadership

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