Abstract
The increased usage of machine learning in healthcare applications requires the highly accurate decision support models which are good in predictive performance and intuitive explanation. Although the models like additive tree could balance both the factors, it can be further enhanced by applying the evolutionary methods. This paper thus provides a decision support system by introducing an enhanced additive tree which could provide an accurate disease prediction in the medical domain. Along with good increase in accuracy, it also scales down the nodes in the tree by performing dimensionality reduction. The proposed model is also as accurate as the linear classifier algorithms and ensemble models.
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Donepudi, B., Narasingarao, M.R., Enireddy, V. (2021). An Evolutionary-Based Additive Tree for Enhanced Disease Prediction. In: Bhattacharyya, S., Nayak, J., Prakash, K.B., Naik, B., Abraham, A. (eds) International Conference on Intelligent and Smart Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 1312. Springer, Singapore. https://doi.org/10.1007/978-981-33-6176-8_3
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DOI: https://doi.org/10.1007/978-981-33-6176-8_3
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