A Hybrid-Based Classification and Regression Model for Predicting Malaria Outbreak

Abstract

Malaria outbreaks remain a main public health challenge worldwide, particularly in Sub Saharan Africa. Fast and correct forecast of malaria outbreaks is critical for permitting timely interventions, decreasing morbidity and death, and ensuring effective sharing of limited healthcare resources. In the last ten years, Data mining and machine learning methods gained widespread attention in complex prediction tasks, such as healthcare analytics, financial and environmental monitoring prediction. Regardless of these improvements, existing malaria outbreak forecast models frequently show limitations in accuracy, adaptability, and applied usefulness. Numerous existing approaches depend solely on either regression or classification approaches, which limits their ability to attain the complex and dynamic interactions between environmental, climatic, and epidemiological factors that affect malaria transmission. This research introduces a new hybrid based predictive model that mix both regression and classification methods in a two-phase framework, marking to enhance the accuracy and reliability of malaria outbreak predictions. The first phase applies a regression model to predict the expected number of malaria cases by examining historical epidemiological data, climate variables and other appropriate environmental indicators. The second phase applies a classification model to determine the likelihood of an outbreak occurring within a given region and time frame, transforming quantitative predictions into actionable early warning signals. Through amalgamation these supplementary methods, the hybrid model influences the strengths of both regression and classification, outcome of in enhanced prediction performance, robustness, and adaptability under diverse outbreak situations. Comprehensive experimentations were done using publicly accessible and region-specific malaria datasets, and the outcomes show that the hybrid model significantly outperforms conventional single-method approaches. The attained model predictive accuracies of 96% through training and 93% in testing, demonstrating strong generalizing capabilities. Likewise, the hybrid approach improves the decision-making aptitudes of healthcare systems by providing timely and reliable information that support evidence-based interventions, such as targeted mosquito control, resource prioritization and prophylactic measures. Study has important inferences for health professionals and authorities, policymakers, and international health organizations endeavoring to reduce malaria burden efficiently. The research helps to healthcare and machine learning fields by giving a scalable and adaptable framework for disease outbreak prediction. It also serves as a foundation for future studies on hybrid and ensemble Machine Learning models, mostly in the perspective of infectious diseases prediction. Future work is endorsed to explore the combination of supervised and unsupervised hybrid methods, integration of real-time epidemiological and climatic data streams, and assessment under large-scale, dynamic outbreak situations. Further, this study emphasizes the capability of hybrid machine learning models to renovate disease outbreak prediction by merging the predictive strengths of regression and classification methods. The findings show the importance of adapting data driven strategies for enabling early detection, timely interventions, public health preparedness and ultimately, the reduction of malaria transmission and its associated health and socioeconomic effects xviii Keywords: Hybrid-Based Model, Classification, Regression, Malaria, Outbreak Prediction