MLP-DT: A Deep Learning Model for Early Prediction of Diabetes and Thyroid Disorders
Abstract
In this paper we present an intelligent and automated system for controlling
diabetes and thyroid disorders. This system is designed to self-diagnose autoimmune
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
diseases. Our system is based on Deep neural networks (DNNs), it contains several
layers and it is classified as MLP (Multi-Layer Perceptron). The proposed
model called Multi-Layer Perceptron model for early prediction of Diabetes and
Thyroid disorders (MLP-DT) uses a set of biomedical variables, allowing the
system to formulate personalized treatment recommendations. To improve diagnostic
accuracy and facilitate 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.
Keywords
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DOI: http://doi.org/10.11591/ijict.v15i2.pp778-788
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