Early Detection of Chronic Diseases Using Patient Data

Abstract

Since CKD is a progressive disease with a high morbidity and mortality rate, its early detection and prediction with precision remain key to the improvement of patient outcomes. Many machine learning and statistical techniques have recently been investigated to improve the diagnosis and prognosis of CKD. Several studies using the UCI CKD dataset have demonstrated how well-suited classical algorithms such as Support Vector Machines, Random Forests, Decision Trees, KNN, and Naïve Bayes are, with an accuracy ranging from 96% to 98.5%. Performance has further been improved with more advanced techniques like XGBoost and the deep learning framework FuDNN-FOSMO, which have been reported to achieve accuracies of up to 99.75%. Besides classification, some regression-based methods (e.g., Random Forest Regression) have also been applied to analyze electronic medical records and have shown a high predictive power (R2 ≈ 0.87) with respect to the progression of chronic kidney disease. Explainable models were validated in multicenter clinical studies in China: XGBoost demonstrated an accuracy of 85.6% and an AUC of 0.91, showing interpretability for clinical adoption. Systematic reviews confirmed the feasibility of risk factor-based screening in primary care; variability across populations was noted, with prevalence rates ranging from 4.4% to 17.1%. Taken together, these studies suggest that while epidemiological screening remains indispensable for more general health planning, machine learning, when combined with strong datasets and optimization of features, offers extremely reliable tools for early detection of CKD