Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
Design of a hybrid system for the diabetes and heart diseases
Scopus
Toplam 377 atıf DOI
Data can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites. © 2007.
Atıf Kaynağı
Atıf Yapan Yayın
A comparative analysis based on feature selection methods and machine learning algorithms for evaluating heart disease prediction performance
Scopus
Havuzumuzda Open Access
Heart disease has become one of the leading causes of death worldwide in recent years. In this study, ML algorithms including KNN, SVM, DT, RF, and LR were employed to predict heart disease status. The classification performance of ML algorithms can be adversely affected by class imbalance and the presence of a large number of features in the dataset. Therefore, the SMOTE was applied to balance the dataset. To identify relevant features, feature selection methods including LASSO, ElasticNet, and LARS were utilized. Classification performance was evaluated using accuracy, precision, recall, F1-score, MCC, n-MCC, and ROC-AUC. Comparative analyses were conducted on a real-world dataset with and without the application of SMOTE and feature selection methods. According to the results, the highest accuracy (0.90), precision (0.89) and recall (0.90) are computed from the RF and LARS+KNN with SMOTE. The highest F1-score (0.90) is handled by the RF model with SMOTE. The highest n-MCC (0.94) and ROC-AUC (0.98) are obtained from the LARS+KNN with SMOTE. It has been observed that the performance of all ML algorithms considered in the study increases significantly when SMOTE is used to address the class imbalance problem and feature selection methods are employed to eliminate irrelevant features.
Atıf Yapan Makale Bilgileri
Kurumlar (5)
Ankara Üniversitesi
Ankara, Turkey
Karatay Üniversitesi
Konya, Turkey
Middle East Technical University (METU)
Ankara, Turkey
Politechnika Poznanska
Poznan, Poland
Selçuk Üniversitesi
Selçuklu, Turkey