A predictive risk level classification of diabetic patients using deep learning modified neural network
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ORIGINAL RESEARCH
A predictive risk level classification of diabetic patients using deep learning modified neural network S. Appavu alias Balamurugan1 · M. Salomi2 Received: 18 January 2020 / Accepted: 20 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In health care firm, data mining (DM) has an effectual role in predicting the diseases. Today, diabetes is the chief global health issue. Several algorithms are introduced for predicting the diabetes disease and its accuracy estimation. Yet, there is no effectual algorithm for providing the severity of diabetes in respect of ratio which interprets the impact of diabetes on different organs of the human body. To overcome such drawbacks, predictive and risk level classification of diabetes patients using DLMNN and Naïve Bayes (NB) classification methods is system model. This system model system comprises 2 phases namely, phase-1: diabetic disease prediction model, and phase-2: risk analysis. In phase-1, the patient data are taken as of the dataset. Then, from this patient dataset repeated data are removed using HDFS Map Reduce (). Next, as the preprocessing stage, the missing attributes are replaced by averaging the considered data. After that, from the preprocessed data the disease is predicted using DLMNN classification method which results in obtaining the diabetic patient data. Then, the diabetic patient data are sent to phase-2. In phase 2, the missing attributes are replaced using the same average method. Next, the patient data is sorted centered on age utilizing recursive K-means clustering algorithm. Finally, the clustered patient data is classified using the NB classifier algorithm. Experiential results contrasted the system model modified deep learning algorithm with the existing IKMC algorithm in rapports of precision, accuracy, F-measure, and recall. The outcomes confirmed that the system model diabetes prediction and analysis model shows better results on considering the existent methods. Keywords Deep learning modified neural network (DLMNN) · Hadoop distributed file system (HDFS) · Naïve bayes (NB) · K-means clustering (KMC) · Improved K-means clustering (IKMC) algorithm
1 Introduction Knowledge discovery of medical data-base is a distinct process. DM is the utmost vital step. DM is the non-trivial extortion of information concerning data (Lekha and Suchetha 2018). This is the procedure of extorting helpful information as of large quantity of databases. It is helpful in the investigative analysis on account of the existence of non-trivial information in a large volume of data and also is * M. Salomi [email protected] S. Appavu alias Balamurugan [email protected] 1
Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, India
Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, India
2
the process of discovering buried patterns that can well be transformed into a vital one (Li et al. 2015). The raw data when transformed to useful and i
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