ML REGRESSION-BASED PREDICTIVE MODELING FOR DISEASE OUTBREAK THRESHOLD ESTIMATION

Abstract

Malaria diagnosis relied heavily on manual microscopy, where a skilled technician examines blood smears under a microscope to identify and count malaria parasites. This method, established in the early 1900s, has been the gold standard for malaria diagnosis but is labor-intensive, time-consuming, and requires significant expertise. In regions with limited healthcare resources, this has often led to misdiagnosis or delayed treatment. The objective of this study is to leverage ML learning techniques to develop an automated, accurate, and efficient diagnostic tool for detecting malaria infections from medical images, thereby improving diagnostic accuracy and reducing the time required for analysis.