Optimizing Healthcare: Enhancing Disease Management with Recommendation Systems

Title

Optimizing Healthcare: Enhancing Disease Management with Recommendation Systems

Creator

Gupta, Ritwika Das; Chatterjee, Soham; Ramadani, Lauresha

Description

This paper explores a data-driven disease recommendation system for medical professionals based on symptoms. The technology examines symptom patterns to recommend diseases from large datasets by utilizing collaborative filtering and data analytics. To provide individualized disease recommendations based on symptom severity, it goes through data preprocessing and uses techniques like collaborative filtering and cosine similarity. Even if the technology is promising, disease predictions might be strengthened. It seeks to support early disease prediction and offer patients and healthcare professionals individualized guidance. This system demonstrates the potential of technology in healthcare decision-making using a basic Tkinter application. More improvements are anticipated as a result of data-driven approach advancements, which will improve patient care and optimize healthcare procedures. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Electrical Engineering;Volume;1269;pp.119-127

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Collaborative filtering; Cosine similarity healthcare optimization; Data analytics; Disease prediction; Disease recommendation system; Healthcare decision-making; Personalized healthcare; Symptom severity; Symptom-based diagnosis

Coverage

Gupta R.D., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Chatterjee S., Department of Statistics and Data Science, CHRIST Deemed to be University, Bangalore, India; Ramadani L., Department of Computer Science, AAB College, Pristina, Kosovo

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 18761100; ISBN: 978-981979514-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-97-9515-4_9

https://www.scopus.com/pages/publications/105003895007?origin=resultslist