A Model for Estimation of Malaria Prediction in North-East Zone, Nigeria (Published)
This study examined Autogressive Integrated Moving Average (ARIMA) model for malaria prediction in the North-Eastern geo-political zone, Nigeria. Cross-sectional research design was adopted in this study as data were collected at a specific period of time. The datasets were collected from Federal Medical Centre, Azare and Federal Teaching Hospital, Gombe, spans through five years (2018 – 2022). The datasets were divided into 80% training set and 20% testing set. ARIMA model was used for estimation and best model was found to be ARIMA(2,2,2). The experiment was conducted in R-Studio. The model was diagnosed and cross-checked for the accuracy using Box-Ljung Statistic, normality curve, ACF, and PACF plots. ARIMA(2,2,2) was used to predict three-year future malaria incidents. The results showed that malaria cases were high in January 2023 with 305 cases (LCI=288 & UCI=898 cases). Also, in year 2024, cases of malaria would be high in December with 38 cases (LCI=653 & UCI=781 cases). Observing year 2025, malaria cases will toll high in December with 53 cases (LCI=714 & UCI=808 cases). It was also discovered that as months of the year increase, the cases of malaria increase. Mean Absolute Percentage Error (MAPE) of the ARIMA(2,2,2) was estimated and yielded 12.43% which implies that the model has 87.57% accuracy. Based on the findings, it is recommended that more treated mosquitoes net and medications should be provided by governments and NGOs to reduce malaria infections in the zone.
Keywords: ARIMA, Estimation, MAPE, Malaria, Model, Prediction, r-studio
Dynamic Decision Tree Based Ensembled Learning Model to Forecast Flight Status (Published)
This paper explains the development of an enhanced predictive classifier for flight status that will reduce over fitting observed in existing models. A dynamic approach from ensemble learning technique called bagging algorithm was used to train a number of base learners using a base learning algorithm. The results of the various classifiers were combined, voting was done, by majority the most voted class was picked as the final output. This output was subjected to the decision tree algorithm to produce various replica sets generated from the training set to create various decision tree models. Object-Oriented Analysis and Design (OO-AD) methodology was adopted for the design and implementation was done with C# programming language. The result achieved was favorable as it was found to predict at an accuracy of 78.3% as against 68.2% accuracy of the existing systems which indicated an enhancement.
Keywords: : Flight Status, Bagging Algorithm, Classification, Ensemble learning, Prediction