PREDICTION OF HEART DISEASE USING CLASSIFICATION AND FEATURE SELECTION METHODS

Akarçay Pervin Ö., Yapici Pehlivan N., Weber G. W.

17th International Symposium on Operational Research in Slovenia, SOR 2023, Bled, Slovenya, 20 - 22 Eylül 2023, ss.289-292 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Bled
  • Basıldığı Ülke: Slovenya
  • Sayfa Sayıları: ss.289-292
  • Anahtar Kelimeler: Adaptive LASSO, Classification algorithms, Elastic Net, Heart Disease, LASSO, Performance metrics
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Heart disease, known as cardiovascular disease has been one of the main causes of death worldwide in recent years. It is affected by various risk factors such as high blood pressure, high cholesterol, diabetes, smoking, obesity, physical inactivity, etc. In this study, Cleveland database of UCI repository of heart disease patients is used. The dataset includes 303 instances and 76 variables which of 14 variables are considered. At first, three different feature selection techniques, namely LASSO, Elastic Net and Adaptive LASSO under specific tuning parameters determined by cross-validation, are applied to reduce the number of independent variables used for the prediction. Then, five different classification techniques, K-nearest neighbor, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression are performed to predict heart disease. Performances of these methods are measured by different metrics, such as accuracy, sensitivity, specificity, and Matthew correlation coefficient.