MLP-DT: a deep learning model for early prediction of diabetes and thyroid disorders - Aouatef Chaib; Ouahiba Djama; Sabar Messaoudi (International Journal of Informatics and Communication Technology (IJ-ICT), 2026)
In this paper we present an intelligent and automated system for controlling diabetes and thyroid disorders. This system is designed to self-diagnose autoim mune diseases as early as possible in order to treat them quickly and thus slow down or stop their progression and thus provide a tool for self-control of dis eases. Our system is based on deep neural networks (DNNs), it contains several layers and it is classified as multi-layer perceptron (MLP). The proposed model called MLP model for early prediction of diabetes and thyroid disorders (MLP DT)uses a set of biomedical variables, allowing the system to formulate person alized treatment recommendations. To improve diagnostic accuracy and facili tate early screening, the system also incorporates machine learning techniques. The optimization in MLP-DT is provided by the adam optimizer algorithm, it is always applied to adjust the weights of the three hidden layers and the output layer (Sigmoid or Softmax). Experimental results demonstrate that the proposed MLP-DT model achieves reliable predictive performance and supports effective early screening of diabetes and thyroid disorders. These findings highlight the potential of the proposed approach as an intelligent decision-support tool for personalized healthcare and preventive medicine.