SMOTE-based Deep LSTM System with GridSearchCV Optimization for Intelligent Diabetes Diagnosis

Abstract

Diabetes is a metabolic illness initiated by either inadequate insulin creation by the pancreas or the body's reduced responsiveness to insulin. It is characterised by consistently high levels of blood sugar and symptoms such as frequent urination, thirst, and increased appetite. Untreated diabetes can result in significant problems that impact crucial organs, presenting potentially fatal dangers. In order to address the need for accurate diagnosis, researchers have utilised artificial intelligence to develop the G-LSTM system. This system employs a novel method that combines SMOTE-based deep LSTM and GridSearchCV optimization to classify diabetes. This technique effectively tackles the issue of class imbalance in diabetes datasets, demonstrating an exceptional level of prediction accuracy. When tested on the PIMA dataset, G-LSTM demonstrated exceptional performance with an accuracy of 97.12%. Additionally, it produced high precision, recall, F1-score, AUC, and MCC values of 97.12%, 0.963, 0.954, 0.887, 0.989, and 0.882, respectively. The results highlight the higher performance of the G-LSTM method compared to other techniques, suggesting its use for clinical investigation of diabetes patients. This innovative intelligent diagnostic framework not only demonstrates the potential of artificial intelligence in healthcare, but also highlights its crucial role in enhancing the precision and effectiveness of diabetes diagnosis and treatment.