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.

中文翻译：

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根据预后标志物的相似性推荐最佳治疗方案\谢谢{所有作者的贡献均等

随着技术领域跨越各个领域的进步，大量信息涌入是不可避免的。在技​​术进步带来的所有机会中，其中之一是提出有效的数据检索解决方案。这意味着从大量数据中，检索方法应该允许用户随着时间的推移获取相关的和最近的数据。在娱乐和电子商务领域，推荐系统已经起到提供上述功能的作用。在医疗领域使用相同的系统肯定会以多种方式证明是有用的。在此背景下，本文的目标是在医疗保健领域提出基于协同过滤的推荐系统，以根据患者的症状推荐治疗方法。此外，开发了一个新的数据集，其中包含有关各种疾病的补救措施，以解决数据可用性有限的问题。所提出的推荐系统接受患者的预后标记作为输入并生成最佳治疗方案。通过几次实验试验，所提出的模型在推荐给定预后标志物的可能补救措施方面取得了有希望的结果。