Improving Medical Diagnosis Systems using Efficient Machine Learning System
DOI:
https://doi.org/10.63665/tmsahr54Keywords:
Machine Learning, Fuzzy Logic, Disease Prediction, Epidemiological Modeling, Clinical Decision Support.Abstract
This paper proposes a unified and mathematically grounded hybrid framework for disease prediction by integrating machine learning, applied mathematics, and fuzzy logic. The study focuses on predictive modeling of infectious and non-communicable diseases, including COVID-19, lung cancer, swine flu, and dengue, using structured clinical, epidemiological, and climatic datasets. Data preprocessing techniques such as cleaning, normalization, and train–test splitting are employed to ensure reliability and consistency. Multiple supervised learning algorithms Support Vector Machine, Random Forest, k- Nearest Neighbors, Decision Tree, and Artificial Neural Network are implemented and comparatively evaluated using accuracy, precision, recall, F1-score, mean squared error, and confusion matrices. To address uncertainty in epidemiological parameters, fuzzy mathematical modeling and bifurcation analysis are incorporated, enabling uncertainty-aware interpretation of disease dynamics through the fuzzy basic reproduction number. Experimental results indicate that Random Forest achieves the most stable and reliable performance, while SVM and ANN show competitive outcomes. Overall, the proposed hybrid analytical–ML framework enhances interpretability, robustness, and scalability, making it suitable for epidemiological studies and clinical decision support systems.
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