Comparative analysis and machine learning predictions of cervical cancer incidence: A multi-national study
1 Biotechnology Program, Department of Mathematics and Natural Sciences, BRAC University, Dhaka, Bangladesh.
2 Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
3 Department of Material Science and Nanotechnology, School of Engineering and Natural Sciences, Kadir Has University, Istanbul Turkey.
4 Department of Biochemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan.
Research Article
World Journal of Biology Pharmacy and Health Sciences, 2024, 18(03), 093–104.
Article DOI: 10.30574/wjbphs.2024.18.3.0328
Publication history:
Received on 23 April 2024; revised on 02 June 2024; accepted on 05 June 2024
Abstract:
This proposal presents a comprehensive investigation of cervical cancer frequency and patterns, with a focus on a multi-national perspective, particularly highlighting the circumstance in Bangladesh. Utilizing a mix of auxiliary information survey, comparative investigation, and predictive modeling, this study sheds light on the worldwide landscape of cervical cancer, emphasizing disparities in rate, screening hones, and healthcare framework. The inquire about utilizes machine learning calculations, especially linear regression, to extend future patterns of cervical cancer in Bangladesh up to 2050. Moreover, an in-depth examination of statistical, clinical, and treatment characteristics of 223 cervical cancer patients in Bangladesh offers basic bits of knowledge into components affecting results. Key discoveries uncover noteworthy fluctuations in treatment and discovery techniques over nations, underscoring the requirement for more harmonized worldwide healthcare approaches. The predictive analysis indicates a potential stabilization in cervical cancer cases in Bangladesh, suggesting a positive trend due to ongoing healthcare efforts. This proposition contributes to the existing body of information on cervical cancer, giving profitable bits of knowledge for healthcare arrangement definition and execution precisely in resource-limited settings.
Keywords:
Cervical Cancer; Predictive Modeling; Machine Learning and Linear Regression; Global Health Disparities; Healthcare Infrastructure
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