Big data analytics in health care by data mining and classification techniques

Abstract

Big data is the compilation of enormous data that arrives from diverse sources for instance online transaction details, social media, sensor data, etc. Such assortment of enormous data developed is tough to evaluate by conventional processing relevance’s. By the development and upcoming latent in the healthcare business field, it is essential to analyze an​ enormous noisy data to get significant information. In healthcare system, the aim of this work is to evaluate the medical database of diabetes patients by a mixture of innovative hierarchical decision attention network, association rules (AR) and multiclass outlier classification with MapReduce framework. The association rule apriori algorithm in a MapReduce framework considers health data to create regulations. This is employed to discover the association among disease and their signs. This examination is made by means of UCI machine learning datasets of diabetes containing 50 attributes. The results of the proposed algorithm are offered by parameters for instance precision, accuracy, recall, and F-score.

Keywords

• Big data
• Data mining
• Apriori algorithm (AA)
• Map Reduce
• Parallel computing