In the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Among health recommender systems, collaborative filtering technique has gained a significant success. However, traditional collaborative filtering algorithms are facing challenges such as data sparsity and scalability, which leads to a reduction in system accuracy and efficiency. In a clinical setting, the recommendations should be accurate and timely. In this paper, an improvised collaborative filtering technique is proposed, which is based on clustering and sub-clustering. The proposed methodology is applied on a supervised set of data for four different types of cardiovascular diseases including angina, non-cardiac chest pain, silent ischemia, and myocardial infarction. The patient data is partitioned with respect to their corresponding disease class, which is followed by k-mean clustering, applied separately on each disease partition. A query patient once directed to the correct disease partition requires to get similarity scores from a reduced sub-cluster, thereby improving the efficiency of the system. Each disease partition has a separate process for recommendation, which gives rise to modularization and helps in improving scalability of the system. The experimental results demonstrate that the proposed modular clustering based recommender system reduces the spatial search domain for a query patient and the time required for providing accurate recommendations. The proposed system improves upon the accuracy of recommendations as demonstrated by the precision and recall values. This is significant for health recommendation systems particularly for those related to cardiovascular diseases.

在过去的几年中，医院已经为患者收集了大量与健康相关的数字数据。这包括临床测试报告，治疗更新和疾病诊断。从该数据中提取的信息用于临床决策和治疗建议。在健康推荐系统中，协作过滤技术取得了巨大的成功。但是，传统的协作过滤算法面临着诸如数据稀疏性和可伸缩性之类的挑战，这导致系统准确性和效率下降。在临床环境中，建议应准确，及时。本文提出了一种基于聚类和子聚类的简易协同过滤技术。拟议的方法论被应用于一组针对四种不同类型的心血管疾病的监督数据，包括心绞痛，非心脏性胸痛，无声缺血和心肌梗塞。根据患者的相应疾病类别对患者数据进行分区，然后对每个疾病分区分别应用k均值聚类。查询过的患者一旦被定向到正确的疾病分区，就需要从减少的子集群中获得相似性评分，从而提高系统的效率。每个疾病分区都有一个单独的推荐过程，这会导致模块化并有助于提高系统的可伸缩性。实验结果表明，提出的基于模块化聚类的推荐系统减少了查询患者的空间搜索范围，并减少了提供准确推荐所需的时间。提议的系统改进了建议的准确性，如准确性和召回率值所表明的。这对于健康推荐系统尤其是与心血管疾病相关的系统非常重要。