Abstract
The current healthcare system is designed using advanced competence technologies such as big data analytics, machine learning, and information technology to provide progressive and intelligent healthcare services. The available healthcare services suffer from various issues such as lack of healthcare data handling, data filtering, and right treatment due to which patients are suffering worldwide. Therefore, this paper proposes a Healthcare multi-phase architecture (HCMP) for predicting chronic kidney disease. The HCMP architecture works on six different layers namely: data-collection, data-storage, data-management, data-processing, data-analysis, and report-generation. The data-storage and data-management layers were performed on heterogeneous Hadoop cluster and the profiling methods were used to consider three situations for calculating the capacity ratio of each DataNode in the cluster. MapReduce is used for parallel data processing and the MySymptom algorithm has been used to filter the kidney dataset of patients according to their symptoms at the data processing layer. Furthermore, horizontal scaling is performed in the Hadoop cluster, and the performance of every DataNode is investigated based on a capacity ratio. The data-analysis layer has performed classification tasks using a Decision tree, K Nearest Neighbors (KNN) classification, Kernel distributed Naïve Bayes, Simple distributed Naïve Bayes, Random forest, and Random tree. Among these classifiers, Kernel distributed Naïve Bayes has produced the best results. Comparing the architecture and results of the present study with the available methods, the empirical results showed an improvement. The significant improvisation of classification performance parameters such as accuracy, precision, and AUC have been improved by 2.2%, 2.3%, and 0.3%, respectively.
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Sisodia, A., Jindal, R. An effective model for healthcare to process chronic kidney disease using big data processing. J Ambient Intell Human Comput 14, 1–17 (2023). https://doi.org/10.1007/s12652-022-03817-w
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DOI: https://doi.org/10.1007/s12652-022-03817-w