• Optimized hybrid learning for multi disease prediction enabled by lion with butterfly optimization algorithm

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    • Keywords


      Disease prediction; multiple disease; Kaggle dataset; UCI repository dataset; optimal feature selection; lion-based butterfly optimization algorithm; Neural network; deep belief network; comparative analysis.

    • Abstract


      As there is a rapid growth in healthcare systems and biomedical data. Machine learning algorithms are utilized in many researches for predicting the risk of the diseases. The major intuition of the present paper is to plan for a novel methodology for multi-disease prediction using deep learning. The overall prediction methodology involves several steps such as ‘‘(a) Data Acquisition, (b) Optimal Feature selection, (c) Statistical eature Extraction, and (d) prediction’’. In the initial step, the medical datasets of diverse diseases is gathered from multiple benchmark sources. Further, the optimal feature selection is applied to the available set of attributes. This is accomplished by hybridizing two meta-heuristic algorithms such as Lion Algorithm (LA), and Butterfly Optimization Algorithm (BOA). In these prediction algorithms, the hidden neuron count of NN andDBN is finely tuned or optimized by the same hybrid Lion-based BOA (L-BOA). The experimental evaluation of various medical datasets validates that the prediction rate of the developed model outperforms several traditional methods.

    • Author Affiliations



      1. ABES Engineering College Ghaziabad, Uttar Pradesh, Ghaziabad 201 009, India
    • Dates

  • Sadhana | News

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      Posted on July 25, 2019

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