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

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Mathematical Modeling and Intelligent Control for Combating Pandemics

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 203))

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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.

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Author information

Authors and Affiliations

  1. Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University, Aligarh, India

    Mohammed Wasid

  2. Department of Computer Science, Aligarh Muslim University, Aligarh, India

    Khalid Anwar

Authors
  1. Mohammed Wasid
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  2. Khalid Anwar
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Editor information

Editors and Affiliations

  1. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

    Zakia Hammouch

  2. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Lahby

  3. Department of Mathematics and Computer Science, Cankaya University, Etimesgut, Türkiye

    Dumitru Baleanu

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Wasid, M., Anwar, K. (2023). Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System. In: Hammouch, Z., Lahby, M., Baleanu, D. (eds) Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer Optimization and Its Applications, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-33183-1_11

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