Biomedical Informatics and Smart Healthcare
ISSN: 3068-5524 (Online)
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TY - JOUR AU - Sharma, Rahul AU - Sharma, Kapil Dev AU - Bijalwan, Anchit PY - 2025 DA - 2025/05/30 TI - HDSF: A Healthcare Decision Support Framework to Provide A Seamless and Adaptable Patient Experience JO - Biomedical Informatics and Smart Healthcare T2 - Biomedical Informatics and Smart Healthcare JF - Biomedical Informatics and Smart Healthcare VL - 1 IS - 1 SP - 1 EP - 8 DO - 10.62762/BISH.2025.352565 UR - https://www.icck.org/article/abs/BISH.2025.352565 KW - healthcare decision support framework (HDSF) KW - healthcare web application KW - online medical services KW - machine learning algorithms in healthcare AB - Healthcare decision support framework(HDSF), a comprehensive web application framework designed to revolutionize healthcare accessibility and efficiency. HDSF integrates various facilities including online appointment booking, virtual doctor consultations, symptom detection, detailed prescription management, home nursing appointment scheduling, and updates on local health camps with Google Maps integration for navigation. The application employs a robust architecture with a front end developed using HTML, CSS, and Bootstrap, while the back-end leverages Java and Java Servlet technologies. Data management is facilitated by MySQL, and the application is developed within the Eclipse IDE and XAMPP environment. Additionally, HDSF incorporates advanced algorithms such as Apriori for association rule learning and K-Nearest Neighbors (KNN) for classification tasks, enhancing its diagnostic and recommendation capabilities. This paper details the development process, system architecture, and algorithmic implementations, highlighting HDSF's potential to improve patient care and streamline healthcare services. SN - 3068-5524 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Sharma2025HDSF,
author = {Rahul Sharma and Kapil Dev Sharma and Anchit Bijalwan},
title = {HDSF: A Healthcare Decision Support Framework to Provide A Seamless and Adaptable Patient Experience},
journal = {Biomedical Informatics and Smart Healthcare},
year = {2025},
volume = {1},
number = {1},
pages = {1-8},
doi = {10.62762/BISH.2025.352565},
url = {https://www.icck.org/article/abs/BISH.2025.352565},
abstract = {Healthcare decision support framework(HDSF), a comprehensive web application framework designed to revolutionize healthcare accessibility and efficiency. HDSF integrates various facilities including online appointment booking, virtual doctor consultations, symptom detection, detailed prescription management, home nursing appointment scheduling, and updates on local health camps with Google Maps integration for navigation. The application employs a robust architecture with a front end developed using HTML, CSS, and Bootstrap, while the back-end leverages Java and Java Servlet technologies. Data management is facilitated by MySQL, and the application is developed within the Eclipse IDE and XAMPP environment. Additionally, HDSF incorporates advanced algorithms such as Apriori for association rule learning and K-Nearest Neighbors (KNN) for classification tasks, enhancing its diagnostic and recommendation capabilities. This paper details the development process, system architecture, and algorithmic implementations, highlighting HDSF's potential to improve patient care and streamline healthcare services.},
keywords = {healthcare decision support framework (HDSF), healthcare web application, online medical services, machine learning algorithms in healthcare},
issn = {3068-5524},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
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