Using Principal Component Analysis and Logistics Regression to Model Major Types of Cancer among Youth A Review

Main Article Content

Mansur Adamu Abubakar
https://orcid.org/0009-0009-1640-6164
M. U. Adehi
M. A. Abubakar

Abstract

Cancer poses a significant health challenge among young Nigerians due to high incidence rates and limited research on risk factors and outcomes. This study examines the utility of logistic regression and principal component analysis (PCA) in understanding cancer patterns, identifying risk factors, and predicting outcomes among Nigerian youth. Logistic regression assesses various factors such as genetics, environment, and lifestyle to predict cancer occurrence, enabling early detection and tailored interventions. PCA aids in uncovering complex data patterns by reducing dimensionality while preserving data variability. Nigeria experiences over 100,000 new cancer cases annually and a rising burden, effective statistical modelling is vital for guiding prevention and intervention strategies. The study compares different cancer types, develops risk assessment models, and forecasts cancer probabilities using logistic regression and PCA. These methodologies hold promise in enhancing cancer comprehension, diagnosis, and management among youth. Patient cancer data from the National Hospital in Abuja and the Aminu Kano Teaching Hospital in Kano were utilized, and selected through convenient sampling for accessibility and convenience. Direct observation served as the primary data collection method. This research underscores the importance of employing statistical modelling techniques, particularly logistic regression and PCA, to address the multifaceted challenges of cancer among youth. Leveraging analytical frameworks can advance early detection initiatives, tailored prevention strategies, and timely interventions, aligned with the region's unique socio-economic and cultural dynamics. Recommendations include prioritizing model validation, fostering collaborative efforts, increasing investment in cancer research infrastructure, and leveraging statistical modelling techniques to enhance cancer epidemiology understanding and healthcare outcomes among youth.

Keywords: Principal Component Analysis, Word Health Organization, Low and Middle Income Countries, Logistic Regression Model, Cancer

Article Details

Using Principal Component Analysis and Logistics Regression to Model Major Types of Cancer among Youth: A Review. (2024). African Journal of Advances in Science and Technology Research, 14(1), 61-72. https://doi.org/10.62154/ee3ykg53
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Copyright (c) 2024 Mansur Adamu Abubakar, M. U. Adehi, M. A. Auwal (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

M. U. Adehi, Nasarawa State University, Keffi, Nigeria.

Department of Statistics,

Nasarawa State University, Keffi, Nigeria.

M. A. Abubakar, Nasarawa State University, Keffi, Nigeria.

Department of Statistics,

Nasarawa State University, Keffi, Nigeria.