Application of naive bayes algorithm for dominant disease classification in coastal environments

Adli Abdillah Nababan

Authors

Adli Abdillah Nababan Bisnis Digital, STMIK Pelita Nusantara, Jl. St. Iskandar Muda No.1, Medan, Indonesia https://orcid.org/0000-0002-1399-6172

Keywords:

Naive Bayes, Disease Classification, Coastal Health, Machine Learning, Epidemiology

Abstract

This research focuses on the implementation of the Naive Bayes algorithm to classify prevalent diseases in coastal areas. Coastal regions, characterized by unique environmental factors and limited healthcare accessibility, pose distinct challenges to public health. The primary objective of this study is to enhance the precision and understanding of disease diagnosis within these regions. By employing data analysis and machine learning techniques, the research aims to contribute significantly to the prevention, management, and treatment of diseases in coastal areas, ultimately improving the well-being of local communities. Additionally, the findings have the potential to assist governments and health institutions in formulating targeted and efficient health policies for coastal areas. A comprehensive understanding of dominant disease patterns enables data-driven decision-making, influencing the allocation of health resources, distribution of vaccines and medicines, and the design of tailored prevention programs. Overall, this research is poised to yield substantial benefits by advancing healthcare and enhancing the quality of life in coastal communities.

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