Recommending Best Course of Treatment Based on Similarities of Prognostic Markers

Abstract

With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to propose efficient solutions for data retrieval. This means that from an enormous pile of data, the retrieval methods should allow the users to fetch the relevant and recent data over time. In the field of entertainment and e-commerce, recommender systems have been functioning to provide the aforementioned. Employing the same systems in the medical domain could definitely prove to be useful in variety of ways. Following this context, the goal of this paper is to propose collaborative filtering based recommender system in the healthcare sector to recommend remedies based on the symptoms experienced by the patients. Furthermore, a new dataset is developed consisting of remedies concerning various diseases to address the limited availability of the data. The proposed recommender system accepts the prognostic markers of a patient as the input and generates the best remedy course. With several experimental trials, the proposed model achieved promising results in recommending the possible remedy for given prognostic markers.

Recommending Relevant Services in Electronic and Mobile Health Platforms

“…Punn et al [8] developed a recommendation system in the healthcare domain, which is based on collaborative filtering and recommends remedies by taking as input the patient's symptoms. Since there is a limited amount of data linking remedies to various diseases that is suitable for creating recommendation systems, the authors also provided a dataset for this purpose.…”

Ensemble Meta-Learning using SVM for Improving Cardiovascular Disease Risk Prediction

“…Researchers have often achieved enhanced performance by combining RF with other methods or utilizing it within hybrid model architectures [21, 22]. Ensemble techniques have gained increasing popularity in healthcare applications, supporting disease diagnosis, risk prediction, and treatment response modeling [23]. Meta-learning advances this concept further, enabling algorithms to “learn how to learn” [24, 25].…”