Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System

Mohammed Wasid and Khalid Anwar
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Mohammed Wasid: Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University
Khalid Anwar: Aligarh Muslim University

A chapter in Mathematical Modeling and Intelligent Control for Combating Pandemics, 2023, pp 197-211 from Springer

Abstract: Abstract Recommender systems (RSs) are personalization tools that reduce information overload and help in decision-making in many real-life situations. RSs are applied in various domains for recommending products and services. Healthcare recommender systems (HRSs) are designed to recommend various health-related services like drug recommendations, doctor recommendations, and disease identification by analyzing the health indicators of the people. During the COVID-19 pandemic, there was a severe shortage of doctors and healthcare facilities, which necessitated the development of intelligent techniques to handle such situations. In this work, a personalized HRS is developed based on the intuitive premise that patients with comparable diseases and health conditions may be exposed to the same risk factors. The developed HRS takes the disease symptoms and previous health records of a patient and matches them with the symptoms of other patients to identify similar patients. After finding similar patients, the HRS recommends the treatment to active patients based on the medicine or treatment prescribed to similar patients in the past. Multiple experiments have been conducted to signify and validate the usefulness of the developed recommender system.

Keywords: Healthcare; Recommender system; Personalization; Collaborative filtering; Fuzzy logic (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-33183-1_11

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DOI: 10.1007/978-3-031-33183-1_11

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