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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | AI-Driven Risk Prediction System for Chronic Diseases: A Supervised Machine Learning Approach Using Electronic Health Records |
| Authors Name: | Mohamed Shafiullah M.N , Mahadevan S , Padmanaban Anand M |
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IJRTI_211544
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| Published Paper Id: | IJRTI2604167 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Chronic diseases cause 71% of global deaths (41 million annually) and consume 75% of healthcare costs. Traditional risk scores (Framingham C-statistic 0.76-0.79, QRISK3 0.86-0.88) demonstrate limited accuracy with linear assumptions and population-specific miscalibration. We developed an AI-driven prediction system using supervised machine learning on publicly available Electronic Health Record datasets (UCI Heart Disease and Pima Indians Diabetes) with clinically relevant features. Methods included comprehensive preprocessing (k-NN imputation, one-hot encoding, z-score normalization, SMOTE balancing), comparison of four algorithms (Logistic Regression, Random Forest, XGBoost, Neural Networks), and stratified 5-fold cross-validation with grid search optimization. XGBoost achieved superior performance: 92.4% accuracy (95% CI: 91.8-93.0%), 91.2% recall, 0.964 ROC-AUC (95% CI: 0.958-0.970), outperforming Framingham by 16 percentage points and QRISK3 by 14 points. SHAP analysis identified clinically meaningful predictors: HbA1c (0.187), age (0.152), systolic blood pressure (0.134), BMI (0.121), fasting glucose (0.108). Risk stratification into low (<0.3, 62% population), medium (0.3-0.7, 28%), and high (≥0.7, 10%) categories demonstrated excellent calibration (Hosmer-Lemeshow p=0.42). Subgroup analysis showed consistent fairness across demographics (all ROC-AUC >0.94). Computational performance of 87ms latency enables real-time clinical deployment. This system provides superior accuracy, interpretability, and fairness for early high-risk identification and targeted prevention. |
| Keywords: | Chronic disease prediction, Electronic health records, Machine learning, XGBoost, Risk stratification, SHAP, Clinical decision support |
| Cite Article: | "AI-Driven Risk Prediction System for Chronic Diseases: A Supervised Machine Learning Approach Using Electronic Health Records", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b211-b217, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604167.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604167
Registration ID:211544
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b211-b217 Country: Chennai, Tamil Nadu, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604167 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604167 |
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ISSN: 2456-3315
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