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ESDC-LSH: Ensemble Support-Vector Deep Convolutional Based Levy Selfish Herd Optimization for Prediction and Classification of Parkinson’s Disease

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Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder caused by a deficiency of dopamine in the brain, which is responsible for motor movements. Early detection of PD helps doctors and patients take proper medication and management strategies. Numerous deep learning-based approaches have been developed by researchers in the medical domain to predict PD. However, existing techniques face complications in achieving greater prediction accuracy due to issues such as class imbalance, small dataset size, high false detection rates, overfitting, training complexity, and others. Therefore, this paper proposes a novel automated PD prediction model using an ensemble support-vector deep convolutional-based levy selfish herd (ESDC-LSH) approach. The input dataset consists of data collected from electronic medical records and wearable sensors. To remove data artifacts, preprocessing steps such as missing value filtration, noise removal, duplicate record elimination, and normalization are performed. After preprocessing, features with valuable information are selected and weighted based on their significance. Using the proposed ESDC-LSH approach, healthy and PD-infected subjects are accurately classified. Additionally, the proposed approach further categorizes diseased subjects based on illness levels, categorized into low, medium, and high risk. Based on the evaluated risk levels, healthcare professionals can use an ontology-based recommendation system to provide suitable medication for the patients. The effectiveness of the proposed ESDC-LSH technique is examined by evaluating measures such as accuracy, precision, Root Mean Square Error (RMSE), sensitivity, F1-score, specificity, and Mean Absolute Error (MAE). Simulation outcomes demonstrate that the proposed ESDC-LSH approach achieves higher prediction accuracy, about 98.8%, precision of about 98.48%, RMSE of about 0.198, sensitivity of about 97.25%, F1-score of about 97.61%, specificity of about 97.45%, and MAE about 0.10 compared to other techniques.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, India

    J. Caroline El Fiorenza & V. Sellam

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  1. J. Caroline El Fiorenza
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  2. V. Sellam
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Correspondence to J. Caroline El Fiorenza.

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Caroline El Fiorenza, J., Sellam, V. ESDC-LSH: Ensemble Support-Vector Deep Convolutional Based Levy Selfish Herd Optimization for Prediction and Classification of Parkinson’s Disease. Wireless Pers Commun 135, 1861–1883 (2024). https://doi.org/10.1007/s11277-024-11173-5

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