Abstract
Nonlinear patterns are challenging to interpret, validate, and are resource-intensive for deep learning (DL) and machine learning (ML) algorithms to predict chronic illness. Transformation of nonlinear features to a linear representation enables the human understanding of AI results and traditional and proven ML algorithms. We propose the counts of terms cross-checked against the chapters of the International Classification of Disease (ICD) to replace the raw representation of key nonlinear variables in health surveys to improve the chronic illness classification performance. The specific selection of nonlinear keywords viz. Male, Female, Diabetes, Cancer, Obese, Overweight, Smoked, Cigarettes, and Sugar from a health survey, transformed into a purely linear and scaled set of features propels the Multinomial Naive Bayes (MNB) algorithm to outperform standard dataset preparation and feature selection methods.
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Jaworsky, M., Tao, X., Yong, J., Pan, L., Zhang, J., Pokhrel, S.R. (2023). Knowledge-Based Nonlinear to Linear Dataset Transformation for Chronic Illness Classification. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_10
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