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Leveraging Supervised Machine Learning for Determining the Link between Suboptimal Health Status and the Prognosis of Chronic Diseases

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All Around Suboptimal Health

Part of the book series: Advances in Predictive, Preventive and Personalised Medicine ((APPPM,volume 18))

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Abstract

Identification of people at risk of cardiometabolic diseases is a major clinical need. Such individuals can benefit from tailored treatments that can potentially reduce their risk, while bringing precision to predictive, preventive and personalised medicine (PPPM). Central to preventive medicine is the new concept of suboptimal health status (SHS), which captures individuals with subclinical conditions across five subscales using a 25-item psychometric-like instrument (SHSQ-25). Each of the subscales represents aspects of a person’s health status that could be explored in a disease continuum. Machine learning (ML) lends itself as a feasible approach to explore SHSQ-25 screening data. It can be used to transform such data into clinically useful information while enabling better health planning, disease forecasting and characterisation of disease risk. ML methods can analyse and interrogate the data in a manner not previously possible with conventional statistical methods. ML algorithms can offer robust and more streamlined means of predicting diseases, identifying individuals with the greatest clinical need and earmarking them for treatment. However, its potential to reveal suboptimal health and cardiometabolic diseases is yet to be explored. This chapter provides an overview of supervised learning, a subset of ML and how it can be applied in subclinical disease prediction.

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

BMI:

Body mass index

DT:

Decision tree

HbA1c:

Glycated haemoglobin

KNN:

k-neural neighbour

ML:

Machine learning

NB:

NaĂŻve Bayes

PCA:

Principal component analysis

PPPM:

Predictive, preventive and personalized medicine

RF:

Random Forest

ROC:

Receiver operating characteristic

SHS:

Suboptimal Health Status

SHSQ-25:

Suboptimal Health Status Questionnaire-25

SVM:

Support vector machine

T2DM:

Type II diabetes mellitus

TC:

Total cholesterol

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Author information

Authors and Affiliations

  1. School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia

    Eric Adua

  2. Rural Clinical Campus, School of Clinical Medicine, Medicine and Health, University of New South Wales, Sydney, NSW, Australia

    Eric Adua

  3. School of Science, Edith Cowan University, Joondalup, WA, Australia

    Ebenezer Afrifa-Yamoah

  4. Department of OMIS, University of Ghana Business School, Accra, Ghana

    Emmanuel Awuni Kolog

Authors
  1. Eric Adua
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  2. Ebenezer Afrifa-Yamoah
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  3. Emmanuel Awuni Kolog
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Corresponding author

Correspondence to Eric Adua .

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Editors and Affiliations

  1. Centre for Precision Health, Edith Cowan University, Perth, Australia

    Wei Wang

Ethics declarations

EA1,2, EA-Y3 and EA4 conceived the study and wrote the manuscript. All authors edited the ideas and concepts presented. All authors read and approved the final manuscript.

Competing Interests

Authors have no competing interest to declare.

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Adua, E., Afrifa-Yamoah, E., Kolog, E.A. (2024). Leveraging Supervised Machine Learning for Determining the Link between Suboptimal Health Status and the Prognosis of Chronic Diseases. In: Wang, W. (eds) All Around Suboptimal Health . Advances in Predictive, Preventive and Personalised Medicine, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-031-46891-9_9

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