Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

大而复杂的数据成为生物医学发现中的宝贵资源，极大地促进了其增加用于检索有用信息的科学资源。然而，在生物医学研究中，从不同的大数据源中索引和检索患者信息是一项挑战。使用MapReduce框架对大数据中的患者信息进行索引和检索。在这项研究中，信息的索引和检索是使用提出的基于Jaya-Sine余弦算法（Jaya–SCA）的MapReduce框架执行的。最初，输入的大数据被随机转发到映射器。计算每个映射器数据的平均值，并将这些数据转发到reducer，在reducer中存储代表数据。对于每个用户查询，输入查询与化简器匹配，从而，它切换到映射器以检索匹配的最佳结果。在基于查询之间的距离从映射器检索数据的同时执行双级匹配。相似性度量是基于启用参数的相似性度量（PESM），余弦相似性和所提出的Jaya–SCA，它是Jaya算法和SCA的集成。此外，提出的Jaya–SCA算法获得了使用StatLog心脏病数据集的F值，召回率和精确度分别为0.5323、0.4400和0.6867。