Diabetes Prediction Using Deep Learning: A Comprehensive Approach Utilizing Feature Selection and Deep Neural Networks

Abstract

Diabetes is a disorder that has a significant impact on world health. In order to properly treat the illness and avoid complications, early identification is crucial. This paper presents a novel scheme for diabetes prediction based on Ant Lion Optimization(ALO)-enhanced deep learning feature selection. We conducted thorough data processing, able to handle missing values, specifying outliers, and validating the Pima Indian's diabetes-relevant data. The selection of pertinent features was optimizedthat use ALO, and the resulting deep neural network (DNN) was then providedwith classification training. The suggested model outperforms typical machine learning (ML) approaches, with an astonishing 96.50% accuracy. This prediction precision demonstrates the aim to expand predictive accuracy by integrating metaheuristic systems with DNNs. According to our findings, this technique is ideal for dramatically enhancing early diabetes diagnosis and delivering valuable knowledge for medical decisions