Disease Prediction Using a Modified Multi-Layer Perceptron Algorithm in Diabetes

This paper presents an adaptation of the Multi-Layer Perceptron (MLP) algorithm for use in predicting diabetes risk. The aim is to enhance accuracy and generalizability model by incorporating preprocessing techniques, dimensionality reduction using Principal Component Analysis (PCA), improvements optimization regularization. Several factors, including glucose level, pregnancy, blood pressure, body mass index, are taken into account when analyzing PIMA Indian Diabetes dataset. Modern methods, dropout regularization, adaptive learning rate incorporated modified MLP fine-tune model's weights boost its predictive abilities. effectiveness evaluated comparing performance with baseline machine methods original terms accuracy, sensitivity, specificity. results this study can improve quality healthcare provided people at risk developing thus contribute development better prediction models disease.