A novel neural-inspired learning algorithm with application to clinical risk prediction

Abstract

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

Evolutionary-based method for risk stratification of diabetic patients

“…The accuracy of the proposed method was 97.833%. Tay et al (2015) propose a new supervised learning algorithm, the artificial neural cell system for classification (ANCSc), which is used for predicting the risk of developing cardiovascular diseases. The algorithm is inspired by the human learning process that is based on three neural processes occurring in the brain: neurogenesis, neuroplasticity, and apoptosis.…”

A probabilistic topic model for clinical risk stratification from electronic health records

“…For example, Fonarow et al, developed a classification and regression tree for risk stratification of patients hospitalized with the Acute Decompensated Heart Failure (ADHF). Their experimental results revealed that ADHF patients at low-, medium-, and highrisk for in-hospital mortality can be easily identified using vital sign and laboratory data obtained on hospital admission [22]. Wang et al, extended the linear regression model for risk stratification that provides clinicians with not only the accurate assessment of a patient's risk but also the clinical context to be acted upon [7].…”

Information Technology in Health Care: Information Retrieval, Processing, and Protection (Review)

“…A computer system is being developed for obtaining and storing information, extracting knowledge from databases, predicting the risk of adverse outcomes with elements of training the system based on neural networks by analogy with natural neurogenesis, apoptosis, neuroplasticity [118,119].…”