A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement
Ashwini B. Sable1, A. S. Kapse2
1Ashwini B. Sable, Student, Department of Computer Science and Engineering, AEC, Chikhli, (Maharashatra) India.
2Dr. A. S. Kapse, HOD, Department of Computer Science and Engineering, AEC, Chikhli, (Maharashatra) India.
Manuscript received on 28 March 2024 | Revised Manuscript received on 08 April 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024 | PP: 47-52 | Volume-13 Issue-4, April 2024 | Retrieval Number: 100.1/ijeat.D443813040424 | DOI: 10.35940/ijeat.D4438.13040424
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Healthcare recommendation systems have garnered significant attention in recent times due to their capacity to improve patient outcomes and treatment. This literature review intends to assess the current state of patient healthcare referral systems by examining relevant studies, techniques, and findings. The report focuses on key research areas, challenges, and viable strategies for the future in the field of patient-centred health recommendation systems. Currently, healthcare administration is in high demand due to its significant advantages in managing hospitals or medical practices. Health management systems are increasingly affecting the entire world daily. The rising demand for healthcare is attributed to various factors, including the availability of healthcare solutions. The health prediction system is an online initiative designed to provide user support and advice. This study proposes a technology that allows consumers to receive immediate online health guidance from an intelligent healthcare system. The system encompasses a multitude of disorders and symptoms associated with different bodily systems. Data mining technologies can be utilised to identify the most probable disease related to a patient’s symptoms. By logging into the system, a doctor can retrieve and review their patient’s information and reports within the doctor’s module. Physicians can analyze the patient’s browsing history and the specific information they are seeking, taking into account their medical prognosis. The doctor has access to his data. The database administrator can incorporate additional disease information, including the type of disease and its associated symptoms. The data mining system runs based on the condition’s name and symptoms. The administrator has access to the database, which includes information on diseases and their associated symptoms. Recommender systems employ diverse machine learning techniques in various domains, such as healthcare recommendation systems (HRS), to advise and promote services or entities to users. Due to the vast array of algorithms documented in the literature, the science of artificial intelligence is now widely employing machine learning techniques in various application domains, including the HRS. Nevertheless, the process of selecting an appropriate machine learning algorithm for a health recommender system seems to be time-consuming.
Keywords: Management, Data mining, Recommendation System, Artificial Intelligence, Domain.
Scope of the Article: Data Mining