Modeling Cancer in Nigeria Using Machine Learning and Sparse Principal Component Regression

This study employs a descriptive research design, specifically a case study approach, to investigate cancer data from Aminu Kano Teaching Hospital in Kano, Nigeria, covering the period from 2013 to 2022.The research focuses on understanding the incidence and mortality rates, demographic patterns, lifestyle and environmental factors, and genetic information associated with cancer among individuals aged 18 to 35 years.By utilizing descriptive statistics, the study identifies the major types of cancer affecting youth in northern Nigeria.Advanced techniques such as Machine Learning and Sparse Principal Component Regression Model were applied to analyze the risk factors related to these cancers, aiming to develop a statistical framework that can improve the accuracy of cancer predictions.The findings indicate a significant prevalence of breast, cervical, leukemia, colon & rectal, and prostate cancers, with varying impacts across different demographic segments.The study's insights offer valuable contributions to enhancing cancer diagnosis, treatment, and management strategies in the region.