STATISTICAL ANALYSIS AND MACHINE LEARNING FOR DISEASE PREDICTION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Early disease prediction can help healthcare professionals identify patients who may require further clinical assessment. The increasing availability of healthcare datasets has created opportunities to combine statistical analysis with machine learning for predictive healthcare. Statistical methods help identify relationships among clinical variables, while machine learning algorithms can detect complex patterns and generate predictions. This paper proposes a framework that combines statistical analysis and machine learning for disease prediction. The framework includes data collection, preprocessing, statistical analysis, feature selection, machine learning, and performance evaluation. Algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and XGBoost can be used for prediction. Evaluation can be performed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed approach can support disease-risk assessment while maintaining attention to data quality, privacy, interpretability, and clinical validation.

References

1. M. Zhang, Y. Zheng, X. Maidaiti, et al., “Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review,” Health Data Science, vol. 4, 2024. doi: 10.34133/hds.0165. (pubmed.ncbi.nlm.nih.gov)
2. L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5–32, 2001.
3. C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, pp. 273–297, 1995.
4. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016.
5. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. World Health Organization, Ethics and Governance of Artificial Intelligence for Health: WHO Guidance, Geneva: WHO, 2021.

Keywords

Disease Prediction, Statistical Analysis, Machine Learning, Healthcare Analytics, Predictive Analytics, Data Science.