Preprint Article Version 1 This version is not peer-reviewed

Deciphering Factors Contributing to Cost-Effective Medicine Using Machine Learning

Version 1 : Received: 29 June 2024 / Approved: 1 July 2024 / Online: 1 July 2024 (15:01:52 CEST)

How to cite: Long, B.; Zhou, J.; Tan, F.; Bellur, S. Deciphering Factors Contributing to Cost-Effective Medicine Using Machine Learning. Preprints 2024, 2024070089. https://doi.org/10.20944/preprints202407.0089.v1 Long, B.; Zhou, J.; Tan, F.; Bellur, S. Deciphering Factors Contributing to Cost-Effective Medicine Using Machine Learning. Preprints 2024, 2024070089. https://doi.org/10.20944/preprints202407.0089.v1

Abstract

This study uses machine learning to identify key factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) from user ratings and prices, we analyzed data from Amazon. The findings indicate that FSA/HSA eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were found to be cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

Keywords

Cost-Effective Medicine; Machine Learning; Cost-Effectiveness Rating (CER)

Subject

Medicine and Pharmacology, Medicine and Pharmacology

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