Artificial Intelligence-Based Hospital Recommendation System

This paper is focused on the development of AI-based hospital recommendation system to enable users find suitable healthcare facilities based on location, ratings, and patient reviews. By leveraging machine learning, the system can analyze the patient data and hospital attributes to provide personalized hospital recommendations. During the system development, hospital data, including location, reviews, and ratings were collected, cleaned and prepared for analysis. Machine learning algorithms, such as collaborative filtering and content-based filtering were employed. The best-performing model was selected based on accuracy metrics to ensure personalized recommendation. The system was implemented using PHP, MySQL, HTML, CSS and JavaScript. It is very helpful in the area of patient decision-making as it provides tailored hospital recommendations.

Authors

Publication Details

Journal
International Journal of Research and Innovation in Applied Science
Published
2025-01-01
DOI
https://doi.org/10.51584/ijrias.2025.10040097
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence-Based Hospital Recommendation System

Uchefuna Charles Ikenna, Chidi Ukamaka Betrand, Oluchukwu Uzoamaka Ekwealor, Charles Austeen Ibeh et al.
International Journal of Research and Innovation in Applied Science
Artificial Intelligence in Healthcare
article

Artificial Intelligence-Based Hospital Recommendation System

Uchefuna Charles Ikenna, Chidi Ukamaka Betrand, Oluchukwu Uzoamaka Ekwealor, Charles Austeen Ibeh, Chiemeka Prince Chukwudum
article en

Abstract

This paper is focused on the development of AI-based hospital recommendation system to enable users find suitable healthcare facilities based on location, ratings, and patient reviews. By leveraging machine learning, the system can analyze the patient data and hospital attributes to provide personalized hospital recommendations. During the system development, hospital data, including location, reviews, and ratings were collected, cleaned and prepared for analysis. Machine learning algorithms, such as collaborative filtering and content-based filtering were employed. The best-performing model was selected based on accuracy metrics to ensure personalized recommendation. The system was implemented using PHP, MySQL, HTML, CSS and JavaScript. It is very helpful in the area of patient decision-making as it provides tailored hospital recommendations.

International Journal of Research and Innovation in Applied ScienceVol. X(IV)
Openalex Percentile: Top 87%
Artificial Intelligence in Healthcare
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