ENHANCING EARLY DEMENTIA PREDICTION USING MACHINE LEARNING

Authors

  • O. C. B. Omankwu Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Umuahai. Abia State. Author
  • M. C. Okoronkwo Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Umuahai. Abia State. Author
  • Chigbundu Kanu Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Umuahai. Abia State. Author

DOI:

https://doi.org/10.60787/jnamp.v67i2.375

Keywords:

Early dementia prediction, Machine learning models, Neuroimaging data, Feature importance

Abstract

Dementia is a rising global health issue affecting millions and imposing significant burdens on families and healthcare systems. Early diagnosis is crucial for better management and treatment outcomes. Traditional diagnostic techniques often detect dementia at later stages, limiting the effectiveness of interventions. This study explores the potential of machine learning to enhance early dementia prediction by analyzing large datasets from various sources, including imaging, genetic, and medical records. By developing and validating a machine learning model, this research aims to improve early diagnosis, enable timely interventions, and ultimately improve patient outcomes.

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References

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Zhang, Z., et al. (2018). Longitudinal data analysis for improved dementia prediction.

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Published

2024-07-31

Issue

Section

Articles

How to Cite

ENHANCING EARLY DEMENTIA PREDICTION USING MACHINE LEARNING. (2024). The Journals of the Nigerian Association of Mathematical Physics, 67(2), 167-172. https://doi.org/10.60787/jnamp.v67i2.375

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