A Decision Support System for Diagnosing Diabetes Using Deep Neural Network

Background and ObjectiveAccording to the WHO, diabetes mellitus is a long-term condition marked by high blood sugar levels. The consequences might be far-reaching. According to current increases in mortality, diabetes has risen to number 10 among the leading causes of mortality worldwide. When used to predict diabetes using unbalanced datasets from testing, machine learning (ML) classifiers and established approaches for encoding categorical data have exhibited a broad variety of surprising outcomes. Early studies also made use of an artificial neural network to extract features without obtaining a grasp of the sequence information. MethodsThis study offers a deep learning-based decision support system (DSS), utilizing bidirectional long/short-term memory (BiLSTM), to accurately predict diabetic illness from patient data. In order to predict diabetes, the BiLSTM hybrid model was used after balancing the data set. ResultsUnlike earlier studies, this proposed model's trial findings were promising, with an accuracy of 93.07%, 93% precision, 92% recall, and a 92% F1-score. ConclusionsUsing a BILSTM model for classification outperforms current approaches in the diabetes detection domain.

Automated summary

This study presents a deep learning-based decision support system (DSS) using BiLSTM to accurately predict diabetes. Compared to traditional methods, this model shows higher accuracy and performance.