Diabetes is one of the most common metabolic diseases, and diagnosis of it is a classification problem. The most challenge is this area is missing value problem. Artificial Intelligence techniques have been successfully implemented over medical disease diagnoses. Classification systems aim clinicians to predict the risk factors that cause diabetes. To address this challenge, we introduce a novel model to investigate the role of pre-processing and data reduction for classification problems in the diagnosis of diabetes. The model has four stages consists of Pre-processing, Feature sub-selection, Classification, and Performance. In the classification technique, ensemble techniques such as bagging, boosting, stacking, and voting were used. We considered both states with/without for pre-processing stage to reveal the high performance of our model. Two experiments were conducted to reveal the performance of the model for the diagnosis of diabetics Mellitus. The results confirmed the superiority of the proposed method over the state-of-the-art systems, and the best accuracy and F1 achieved 97.12% and 97.40%, respectively. پرونده مقاله

Chronic Kidney Disease is one of the most common metabolic diseases. The challenge in this area is a pre-processing problem. Artificial Intelligence techniques have been implemented over medical disease diagnoses successfully. Classification systems aim clinicians to predict the risk factors that cause Chronic Kidney Disease. To address this challenge, we introduce an effective model to investigate the role of pre-processing and machine learning techniques for classification problems in the diagnosis of Chronic Kidney Disease. The model has four stages including, Pre-processing, Feature Selection, Classification, and Performance. Missing values and outliers are two problems that are addressed in the pre-processing stage. Many classifiers are used for classification. Two tools are conducted to reveal model performance for the diagnosis of Chronic Kidney Disease. The results confirmed the superiority of the proposed model over its counterparts. پرونده مقاله