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RETRACTED ARTICLE: Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

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This article was retracted on 13 September 2022

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Abstract

Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.

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Authors and Affiliations

  1. School of Information Technology and Engineering, VIT University, Vellore, India

    Gunasekaran Manogaran & M. K. Priyan

  2. Sri Ramanujar Engineering College, Chennai, India

    R. Varatharajan

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  1. Gunasekaran Manogaran
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  2. R. Varatharajan
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  3. M. K. Priyan
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Correspondence to M. K. Priyan.

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Manogaran, G., Varatharajan, R. & Priyan, M.K. RETRACTED ARTICLE: Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System. Multimed Tools Appl 77, 4379–4399 (2018). https://doi.org/10.1007/s11042-017-5515-y

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  • DOI: https://doi.org/10.1007/s11042-017-5515-y

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