Cost-Effectiveness of the 3E Model in Diabetes Management: A Machine Learning Approach to Long-Term Economic Impact

Supriya Raghav^1*Santosh Kumar¹Hamid Ashraf²Poonam Khanna³

• ¹Poornima University, Jaipur, India
• ²Aligarh Muslim University, Aligarh, Uttar Pradesh, India
• ³Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, Haryana, India

This study investigated the cost-effectiveness and clinical impact of the 3E (Education, Empowerment, and Economy) model in diabetes management using advanced machine learning techniques. We conducted an observational longitudinal descriptive analysis on 320 patients, grouped to intervention and control participants, over 24 months. The 3E model demonstrated significant cost reductions, with the intervention group achieving a 74.3% decrease in total costs compared to 41.8% in the control group, while maintaining equivalent glycemic control.Machine learning models, including Random Forest and K-means clustering, identified key factors influencing treatment costs and patient subgroups most responsive to the intervention.Natural Language Processing techniques revealed medication patterns associated with greater cost reductions. Long-term projections using ensemble methods (XG Boost, Exponential Smoothing, and Prophet) predicted that on average, each year contributes 20% to the total cumulative savings over 5 years. No significant correlations between cost reduction and socioeconomic factors, gender, or age, suggesting broad applicability of the 3E model were observed. These findings demonstrated the potential of the 3E model in significant reduction in diabetes management costs without compromising care quality, highlighting its value for healthcare policy and resource allocation in managing chronic diseases.