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RETRACTED ARTICLE: Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system

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This article was retracted on 30 May 2022

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Abstract

In this digital world, data is an asset, and enormous data was generating in all the fields. Data in the healthcare industry consists of patient information and disease-related information. This medical data and machine learning techniques will help us to analyse a large amount of data to find out the hidden patterns in the disease, to provide personalised treatment for the patient and also used to predict the disease. In this work, a general architecture has proposed for predicting the disease in the healthcare industry. This system was experimented using with reduced set features of Chronic Kidney Disease, Diabetes and Heart Disease dataset using improved SVM-Radial bias kernel method, and also this system has compared with other machine learning techniques such as SVM-Linear, SVM-Polynomial, Random forest and Decision tree in R studio. The performance of all these machine learning algorithms has evaluated with accuracy, misclassification rate, precision, sensitivity and specificity. From the experiment results, improved SVM-Radial bias kernel technique produces accuracy as 98.3%, 98.7% and 89.9% in Chronic Kidney Disease, Diabetes and Heart Disease dataset respectively.

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Authors and Affiliations

  1. Anna University, Chennai, Tamil Nadu, India

    Karthikeyan Harimoorthy

  2. Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India

    Menakadevi Thangavelu

Authors
  1. Karthikeyan Harimoorthy
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  2. Menakadevi Thangavelu
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Correspondence to Karthikeyan Harimoorthy.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-03971-1

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Harimoorthy, K., Thangavelu, M. RETRACTED ARTICLE: Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system. J Ambient Intell Human Comput 12, 3715–3723 (2021). https://doi.org/10.1007/s12652-019-01652-0

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  • DOI: https://doi.org/10.1007/s12652-019-01652-0

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