The Role of Information and Communication Technology for Effective Crime Detection and Prevention In Tanzania Police

Abstract

The purpose of the study was to perform prediction of maternal outcome in Tanzania using Machine Learning Classifiers encompassing of Logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RFF). The study employed a retrospective cross-sectional research design and collected secondary binary data covering a period of three years from 2020 to 2022 whereby a total of 76,227 women were recorded in The Health Information Management System (HIMS), so the sample of the study. The finding suggested that maternal mortality is likely to decrease in future because the survival cases are predicted more with little evidence of death cases. On the other hand, the study logistic regression, Artificial Neural Network, and Support Vector Machine predicted maternal outcome accurately by 99.36%, 93.36%, and 99.96% respectively while, Random Forest predicted accurately by 100.0%. In that regard, the random forest model and its derived mathematical equation is suggested ad the best model for predicting maternal outcome in Tanzania. The study underscores the complexity of maternal mortality and encourages the development of targeted interventions and predictive models tailored to the Tanzanian context. Also, the study recommended that cautions should be taken when suggesting the machine learning predictive models to avoid overfitting.