- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Recently, diabetes becomes the widespread and major disease in the world. In this paper, we propose a novel hybrid classifier for diabetic diseases. The proposed hybrid classifier named Logistic Adaptive Network-based Fuzzy Inference System (LANFIS) is a combination of Logistic regression and Adaptive Network-based Fuzzy Inference System. Our proposed intelligent system does not use classifiers to continuous output, does not delete samples with missing values, and does not use insignificant attributes which reduces number of tests required during data acquisition. The diagnosis performance of the LANFIS intelligent system is calculated using sensitivity, specificity, accuracy and confusion matrix. Our findings show that the classification accuracy of LANFIS intelligent system is about 88.05%. Indeed, 3–5% increase in accuracy is obtained by the proposed intelligent system and it is better than fuzzy classifiers in the available literature by deleting all samples to missing values and applying traditional classifiers to different sets of features.
In this paper, we proposed a novel intelligent system for diagnosing diabetic diseases with missing values in clinical attributes. Our studies investigated the multiple imputation techniques for predicting missing values, orthogonal transformation techniques for reducing the dimension of input data and applying a novel hybrid classifier. The proposed classifier is combination of logistic model and adaptive network based on fuzzy inference system.
The high accuracy was obtained by LANFIS classifier and applying the subtractive clustering method to generating fuzzy rules. The best classification accuracy rate of the proposed intelligent diagnosis system is about 88.05% for diabetic data. The results of our examinations show that the proposed method significantly enhances the performance in comparison with related methods. Another advantage of our novel intelligent system is that it does not necessitate doctors to examine more tests.