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Journals / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 3

Analysis of Disease Cases Data Across the Nation Using Machine Learning Algorithms and Its Integration with Healthcare Supply Chain Management Industries

Pradeep Sharma(NIT KURUKSHETRA)0000-0001-5906-9916
Puran Chandra Tewari(NIT KURUKSHETRA)0009-0005-0322-2615
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The current research work introduces the scope for creating more resilient and amenable processes in the healthcare system.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nowadays, a more prompt response is required to combat public fear of disease epidemics by adopting more resilient healthcare supply chain management.
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Key Findings & Data:We have also used linear regression to calculate the coefficient of determination(R2) of all states to scale our research to the national level(India level).
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Conclusion & Impact:The current research work introduces the scope for creating more resilient and amenable processes in the healthcare system.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Dengue · Healthcare · Machine learning

Abstract

Nowadays, a more prompt response is required to combat public fear of disease epidemics by adopting more resilient healthcare supply chain management. Among prominent statistical techniques, random forest classification and gradient boosting provide goodness-of-fit and indicative estimates of the outbreak of a pandemic disease. The current study presents a data-driven approach as an information channel for improving supply chain logistics in healthcare and medical services. The research focuses on dengue fever cases in India and related states, analyzing data from past years to predict and identify patterns using mathematical techniques. The current research analyzed potential benefits and predictions using Machine Learning techniques and their integration with fuzzy logic for the supply chain distribution of medicines in healthcare industries, thereby reducing lead times and controlling costs compared to conventional approaches. We have also used linear regression to calculate the coefficient of determination(R2) of all states to scale our research to the national level(India level). This helps indicate how precise our indicative predictions are and analyzes the variance in our dataset. The study involves indicative forecasting rather than precise forecasting regarding such epidemic outbreaks, which supports improving supply chain management across healthcare and medical service logistics. The estimated consumption of the medical supplies for the next year is estimated using fuzzy logic mechanisms. As a result of this approach, public health outcomes improve in their effectiveness. This study paves the way for advanced machine learning methods and regional prognostic models. The current research work introduces the scope for creating more resilient and amenable processes in the healthcare system.

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Cite This Article

Sharma, P., Tewari, P. C. (2026). Analysis of Disease Cases Data Across the Nation Using Machine Learning Algorithms and Its Integration with Healthcare Supply Chain Management Industries. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 3. https://doi.org/10.35377/saucis...1883802

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