Abstract
Explainable artificial intelligence (XAI) becomes a hot research topic in the domain of biomedical and healthcare applications. Owing to the benefits of handling massive and complicated data, XAI concept finds useful in several applications, particularly health care. With the developments of machine learning (ML) and XAI, healthcare service quality can be considerably improved. This article designs an optimal boosting label weighting extreme learning machine for mental disorder prediction and classification (OBWELM-MDC) technique. The goal of the OBWELM-MDC technique is to determine the different levels of DAS. In addition, the OBWELM-MDC technique involves the design of boosting label weighted extreme learning machine (BWELM) model for prediction process. Besides, the BWELM model can be derived by the incorporation of the label weighted extreme learning machine (LW-ELM) with boosted ensemble learning model. Moreover, the parameter tuning of the BWELM model takes place by the use of chaotic starling particle swarm optimization (CSPSO), where the inertia weight and acceleration coefficient of the PSO algorithm are modified via logistic chaotic map. The application of CSPSO algorithm has improved the predictive performance of the BWELM model. The experimental result analysis of the OBWELM-MDC technique takes place using benchmark dataset, and the results are examined under several measures. The experimental results showcased that OBWELM-MDC technique has accomplished maximum predictive outcomes over the other methods.
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Laxmi Lydia, E., Anupama, C.S.S., Sharmili, N. (2022). Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification. In: Khamparia, A., Gupta, D., Khanna, A., Balas, V.E. (eds) Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI). Intelligent Systems Reference Library, vol 222. Springer, Singapore. https://doi.org/10.1007/978-981-19-1476-8_1
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