| Abstrakt: |
Globally, diabetes mellitus is the most dangerous disease and it is important to predict disease at an early stage to treat the disease. The learning-based algorithms play a significant part in supporting decision-making in diabetes prediction as well as diagnosis. Machine Learning (ML) based diabetes classification suffers from poor performance due to constraints such as limited labeled data and the challenge of data imbalance. Therefore, this research proposes the Self-adaptive Evolutionary Algorithm (SAEA) with Convolutional Neural Network (CNN) for the prediction and classification of diabetes. For validating the proposed method's effectiveness, the data is collected from benchmark datasets such as the Pima Indian Diabetes dataset (PIDD), Frankfurt Hospital, Germany, and North California State University (NCSU). Then, the min-max normalization is used for normalizing the data in the preprocessing step. The Chi-square-based feature selection technique is used for selecting the best feature for categorial features and finally, the SAEA with CNN is used for classification and it classifies the disease into diabetes and non-diabetes. The proposed method's effectiveness is estimated by using various matrices and it attains the accuracy of 99.87% and 99.99% by using PIDD and German datasets when compared to existing approaches like Deep Neural network (DNN), CNN, and Deep CNN (DCNN). [ABSTRACT FROM AUTHOR] |
