ABSTRACT

The study found a clear association between complications of chronic diseases and a number of clinical factors and biomarkers. The most important independent risk factors of complications are chronic kidney disease (CKD), hypertension and low haemoglobin levels. We also found a positive correlation between the ferritin level and the incidence of complications. This correlation can reflect the role of inflammation and iron metabolism disorders in the progression of the disease. Statistical analysis showed that one of the strongest predictors of complications was inversely related to haemoglobin. By logistic regression, CKD was the most influential factor of all variables studied. Complications were more strongly related to disease status and vital signs than to demographic factors, because neither age nor smoking had a significant effect after adjustment for other factors. The Random Forest algorithm was the most accurate and the one with the highest area under the area of the ROC curve among all machine learning models, and the hybrid model that included clinical data and biomarkers was the best predictive model, suggesting the importance of the integration of different types of data to increase the model predictive power.The variable importance analysis indicated that haemoglobin was the most significant variable for predicting complications, followed by ferritin and CKD, emphasising the importance of haematological markers in assessing the progression of chronic diseases. In addition, the interaction analysis showed a significant co-effect between low haemoglobin and chronic kidney disease, indicating that the presence of both factors would further increase the likelihood of complications. In general, the study confirms that the application of logistic regression techniques with machine learning algorithms is an effective and accurate tool for the early prediction of chronic disease complications, which can contribute to improving clinical decision-making, guiding early therapeutic interventions and reducing the health burden on patients and the health system.

KEYWORDS 

Chronic diseases, disease complications, logistic regression, machine learning, predictive models, biomarkers, random forest, ROC analysis, risk prediction, medical artificial intelligence.

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Cite this article

Neamah, M. H. (2026). Predicting Chronic Disease Complications using Clinical Data and Biomarkers: A Logistic Regression and Machine Learning Approach. INTERNATIONAL JOURNAL OF HEALTH & MEDICAL RESEARCH, 5(8), 738-744. https://doi.org/10.58806/ijhmr.2026.v5i8n03