2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 341-366, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sudhirvarma Sagiraju 1 , Jnyana Ranjan Mohanty 2 , Anima Naik 3 *
Doi: https://doi.org/10.54216/FPA.190225
Accurate disease prediction is essential for enabling preventive healthcare and reducing the burden of chronic illnesses. This study introduces an innovative multi-disease prediction framework leveraging machine learning and optimization techniques to address limitations in precision and scope present in prior research. Specifically, we focus on predicting five major diseases—diabetes, heart disease, kidney disease, liver disease, and breast cancer—by employing the Social Group Optimization (SGO) algorithm to fine-tune the Random Forest (RF) classifier's hyperparameters.The proposed SGO-optimized RF model optimizes seven critical parameters - n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, bootstrap, and criterion simultaneously, significantly enhancing the model's performance. Our approach, applied to five disease datasets, achieves notable accuracy improvements: 98.25 When tested on diverse datasets, the model achieves exceptional accuracy: 98.25% for breast cancer, 84.62% for liver disease, 93.44% for heart disease, 82.47% for diabetes, and 100% for chronic kidney disease. On average, the SGO-optimized RF outperforms existing methods with a 2.3% accuracy improvement across datasets. This research highlights the transformative potential of SGO-based optimization in advancing the accuracy and reliability of predictive models. The results demonstrate the framework's applicability in clinical scenarios, providing precise and actionable insights that support early diagnosis and improve patient outcomes.
SGO , Random forest , Accuracy , Hyperparameters , Healthcare , Chronic disease prediction
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