QUADRATIC MAPREDUCE DOMAIN ADAPTIVE DISCRIMINATIVE AI FOR DIABETES DISEASE PREDICTION
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Abstract
Modern healthcare monitoring systems play a vital role in enhancing patients’ quality of life by facilitating continuous examination of their health conditions. In healthcare industry, diabetes mellitus is a key risk to public health, and early detection is essential for efficient care and issue prevention. Although numerous deep learning methods have explored smart healthcare systems for diabetes prediction, achieving high accuracy along with time-efficient prediction remains a significant challenge.In order to address these challenges, a novel method called Quadratic MapReduce domain adaptive Discriminative Artificial Intelligence (QMDADAI) is introduced. The main objective of QMDADAI model is to perform accurate diabetes disease prediction with high accuracy and minimal time consumption in healthcare monitoring system. The Discriminative AI model comprises of various processing steps, namely data acquisition, preprocessing, feature selection and classification to enhance the performance of diabetes disease prediction. In the data acquisition phase, the numerous patient data samples are collected from the dataset. Subsequently, the data pre-processing stage is carried out which includes two major processes namely missing data handling and outlier data removal from the input dataset. After data pre-processing, the more important feature selection process is carried out using MapReduce framework from the input dataset. Once the features selected, the classification process is carried out using sequential rank correlation for diabetes disease prediction with higher accuracy. The fine tuning process of Discriminative AI is done using Bats Echolocation optimization algorithm for minimizing the error and enhancing the accuracy of the diabetes disease prediction.Experimental assessmentof QMDADAI model is conducted with different evaluation metrics such as accuracy, precision, recall, F1 score, specificity, ROC-AUC, confusion matrix and training time. The results indicate that the proposed QMDADAI model achieved higher accuracy in diabetes disease prediction with minimal time consumption compared to existing deep learning methods.
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