A Bi-Model Machine Learning Driven Application for Diagnosing the Dominant Illness among Typical Nigerian University Students

Abstract

The implementation and deployment of machine learning models for the diagnosis of dominant illnesses among students require significant investment in technology and infrastructure, which is among the barriers for healthcare organizations with limited resources. In order to increase its adoption, this research suggests the creation of a Bi-Model Machine Learning Driven Application that will enable university students to get diagnosed with common ailments. The plan is to apply a high-level model using a hybrid methodology that combines the development of Machine Learning Models with Agile Software Development. In order to do this specifically, Python was used to implement exploratory data analysis, classification, and regression models, as they have proven to be highly effective in both diagnosing the primary illness and predicting the length of hospital stay. The bi-model were built with four different algorithms each, so as to adopt the ones with best performance for the deployment. The model built with Gradient Boosting Classifier has 100% accuracy, 100% precision, 100% recall as compared to other three algorithms through three repeated training of the model. On the prediction of admission duration task, Gradient Boost Regression works best, and this is because it has the least Root Mean Square Error of 0.57 and Mean Absolute Error to be 0.423 among other compared three algorithms, as measured. This was achieved through the use of fresh localized dataset from the Federal University Lokoja Health Center, which was pre-processed, and stored in the file manager/internal storage for visualization and modelling. Furthermore, the completed models was deployed to a web application using flask and Mysql Lite Database. In the end, the application reduced human error in diagnosis and care management of the student population while they are pursuing their education by enabling evidence-based awareness, educated public health policy, and individualized treatment.