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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.

Journal of Biomedical Informatics

|January 11, 2015

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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.

Keywords:

Cardiovascular disease Classification Clinical risk prediction Neural-inspired algorithms

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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.