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Exploring HCP Influence Through Machine Learning: A Predictive Analysis of Lyumjev Prescribing Trends in the North Zone in India
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 4 |
| Year of Publication: 2025 |
| Authors: Gourav, Shairy, Kamalpreet Kaur |
10.5120/ijca2025924841
|
Gourav, Shairy, Kamalpreet Kaur . Exploring HCP Influence Through Machine Learning: A Predictive Analysis of Lyumjev Prescribing Trends in the North Zone in India. International Journal of Computer Applications. 187, 4 ( May 2025), 21-26. DOI=10.5120/ijca2025924841
Abstract
The understanding of the prescribing conduct of the Healthcare Professionals (HCPs) and segmentation over the adoption and the use of medicinal drugs. This study explores the effects of Healthcare professionals on the prescription patterns of the Lyumjev a quick-acting insulin variant, in Northen Region in India. The study employs machine learning methods and models to forecast Lyumjev prescription behavior, examining the fundamental components of the influence of HCP decision-making [6]. By analyzing prescription data, patient demographics, HCP specialties, and clinical recommendations, the study aims to find the connections between each of these factors and the likelihood of prescribing Lyumjev. The research and the study leverage the machine learning models, and algorithms, including decision tree, random forest classifier (RFC), and support vector machine (SVM) to make the predictive model. These models are trained and tested by using real-life data which is filled by the medical representatives while making the deliveries of the Lyumjev to the North region doctors. The prescribing data can make the future trends of the HCPs influence. Distribution is identified as a significant predictor of prescribing behavior [3]. The study also explores the role of clinical guidelines and treatment protocols in shaping prescription choices. The findings provide valuable insights into how HCP characteristics influence their prescribing habits and the factors driving the adoption of Lyumjev over other insulin analogs. Furthermore, the paper provides the predictive abilities of the machine learning models and the methodologies not only in the healthcare sector but also in the marketing of medicines. The findings have practical relevance and the results for the pharmaceutical companies where they can refine their marketing as well as HCPs to influence.
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