Automated Prediction of Multiple Diseases through Deep Learning Models
DOI:
https://doi.org/10.63282/3050-922X.ICAILLMBA-105Keywords:
Multi-Disease Prediction, Deep Learning, Multi-Layer Perceptron, Blood Parameter Analysis, Healthcare AnalyticsAbstract
Accurate and early disease prediction is essential in modern healthcare due to the rising prevalence of chronic illnesses. This project presents a deep learning framework for predicting multiple diseases designed to improve diagnostic accuracy and efficiency using advanced neural network models. The system applies effective preprocessing techniques, including normalization and Z-score scaling, to enhance feature representation of critical health parameters such as glucose, hemoglobin, cholesterol, and blood pressure. A Multi-Layer Perceptron (MLP) model is employed because of its capacity to learn intricate non-linear patterns while maintaining a simpler and efficient architecture compared to deep feedforward networks. Model training is performed with the Adam optimizer and validated through cross-validation to ensure robustness across diverse patient data, resulting in an accuracy of 95.13%. The proposed system is implemented using Python with TensorFlow, pandas, NumPy, and scikit-learn libraries on a Windows platform.
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