International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Data-Driven Predictive Modeling For Opioid Overdose Risk Dr. Kala Venugopal Associate Professor & HOD Dept of ISE Acharya institute of technology Affiliated to VTU, Belagavi - 590018 Bengaluru – 560107
Abhigna D P Dept of ISE Acharya institute of technology Affiliated to VTU, Belagavi - 590018 Bengaluru – 560107
Manjunath Dept of ISE Acharya institute of technology Affiliated to VTU, Belagavi 590018 Bengaluru – 560107
Ankitha Divitar Dept of ISE Acharya institute of technology Affiliated to VTU, Belagavi - 590018 Bengaluru – 560107
Muli Pujitha Dept of ISE Acharya institute of technology Affiliated to VTU, Belagavi 590018 Bengaluru – 560107
---------------------------------------------------------------------------***-------------------------------------------------------------------------Abstract—This paper introduces a Drug Overdose dynamic changes in behavior, environmental factors, or Prediction System (DOPS) that leverages machine learning to predict mortality rates based on demographic and clinical data. The system is designed to provide early warnings, support decision-making, and improve public health response strategies. DOPS incorporates key components to improve predictive accuracy and provide early warnings for overdose risks. It begins with data preprocessing and feature engineering to ensure highquality input data, followed by predictive modeling that analyzes overdose risk patterns using machine learning techniques. Demographic clustering helps identify highrisk populations based on factors such as age, gender, and other characteristics. A real-time prediction engine continuously monitors emerging overdose trends, enabling timely detection of potential risk spikes. By integrating real-time analytics, DOPS enhances overdose prevention by shifting from reactive to proactive strategies.
Introduction The opioid epidemic remains a pressing global health concern, leading to a continuous rise in overdose-related deaths [9]. The misuse of opioids, including prescription pain relievers and synthetic drugs like fentanyl, significantly burdens healthcare systems [2]. Despite ongoing efforts to mitigate this crisis, conventional surveillance and intervention methods often rely on historical data and manual reporting, resulting in delayed results [4]. These traditional approaches focus on retrospective analysis rather than proactive prevention, limiting their effectiveness in reducing overdose incidents and improving patient outcomes [4].. Current methods depend on static risk assessments and generalized models that do not consider
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emerging drug trends.
As a result, healthcare professionals and policymakers struggle to deploy timely interventions and allocate resources efficiently. There is a growing need for data-driven, predictive solutions that can analyze vast amounts of information and provide early warnings to prevent fatal overdoses. Machine learning (ML) offers a transformative approach to overdose prevention by detecting hidden patterns, analyzing risk factors, and making accurate predictions based on large-scale datasets. Unlike conventional statistical models, ML algorithms can process complex relationships between demographic, clinical, and behavioral factors, enabling healthcare systems to identify individuals or populations at increased risk [7]. By leveraging predictive analytics, public health agencies can implement targeted intervention strategies, optimize resource distribution, and improve the effectiveness of harm reduction programs. This paper introduces the Drug Overdose Prediction System (DOPS), a machine learning-based framework designed to forecast overdose risks based on demographic and clinical data. The system is structured to provide early warnings, support datadriven decision-making, and enhance public health strategies. The core functionalities of DOPS include: Data Preprocessing & Feature Engineering – Refining and structuring data to improve accuracy and reliability [6]. Predictive Modeling – Applying advanced machine learning techniques to assess overdose risk levels [7]. Demographic Clustering – Grouping individuals based on socio-demographic patterns to identify high-risk populations.
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