Malaria Disease Prediction Using Frequency-Based Machine Learning Algorithms and Ensembles Algorithms

Malaria is one of the most serious and threatening diseases in Sub-Saharan Africa. Its cases increase drastically during the rainy season. It can spread throughout an infected person's entire body in less than an hour. The diagnosis process is time-consuming, and its accuracy is negatively affected due to the lack of technical tools and infrastructure in many laboratories in malaria-prone countries. Hospitals, however, keep patient data during the diagnosis period. Due to the vast amount of diagnostic data gathered and stored on a daily basis in various mediums such as files, it has become increasingly crucial to develop powerful techniques for analysing and interpreting the data. This analysis aims to extract meaningful knowledge and insights that can significantly contribute to malaria diagnosis and decision-making processes. This paper selected four frequency-based algorithms, namely Naïve Bayes, (J48) Decision Tree, ZeroR, and the OneR algorithm, to develop a hybrid model for frequency-based classification algorithms using the available dataset of malaria diagnoses collected from the Federal Medical Centre in Yola, Adamawa state. The research results indicate that the highest accuracy, 88.4%, was achieved by the (J48) Decision Tree model. Additionally, this research employed ensemble methods to enhance the performance of each classification model. The results demonstrated that the accuracy of the Decision Tree and ZeroR models remained the same at 88.4% and 60.9% before and after boosting, respectively. In contrast, the accuracy of the Naïve Bayes and OneR models increased from 79.9% to 87.0% and from 79.8% to 87.6% before and after boosting, respectively. In conclusion, among the frequency-based classification models, the Decision Tree model consistently exhibited the highest accuracy both before and after boosting.