Related Experiment Video
Updated: Apr 18, 2026
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
A novel neural-inspired learning algorithm with application to clinical risk prediction
Darwin Tay1, Chueh Loo Poh2, Richard I Kitney3
1Department of Bioengineering, Imperial College London, UK; Division of Bioengineering, Nanyang Technological University, Singapore.
Insights
A new Artificial Neural Cell System for classification (ANCSc) algorithm shows superior performance in predicting cardiovascular disease (CVD) risk compared to existing methods. This novel approach identifies key clinical markers for better risk assessment and management strategies.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Artificial Intelligence
Background:
- Clinical risk prediction is crucial for managing diseases like cardiovascular disease (CVD), a leading cause of death.
- Machine learning models are vital for accurate risk prediction, but their performance depends on the learning algorithm used.
Purpose of the Study:
- To introduce a novel neural-inspired algorithm, the Artificial Neural Cell System for classification (ANCSc), for enhanced cardiovascular disease risk prediction.
- To develop and evaluate a CVD risk prediction tool utilizing the ANCSc algorithm.
Main Methods:
- The ANCSc algorithm was developed, inspired by brain mechanisms like neurogenesis, neuroplasticity, and apoptosis.
- Benchmark testing was performed using the Honolulu Heart Program (HHP) dataset.
- ANCSc performance was compared against Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS).
Main Results:
- The ANCSc algorithm statistically outperformed both SVM and EDC-AIRS in CVD risk prediction.
- Key clinical markers identified by ANCSc include diet/lifestyle factors, pulmonary function, medical history, blood data, blood pressure, and electrocardiography.
- Identified markers are clinically significant, offering potential for future clinical trials.
Conclusions:
- The ANCSc algorithm presents a promising advancement in machine learning for clinical risk prediction, particularly for cardiovascular disease.
- This novel approach can aid in identifying individuals at high risk for CVD, facilitating timely interventions and improved patient outcomes.
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.
