Machine Learning for Health Insurance Prediction in Nigeria

Health insurance coverage remains critical to healthcare accessibility, particularly in developing nations like Nigeria. This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. The dataset utilized for analysis comprises demographic information, socioeconomic factors, and health-related variables collected from a diverse sample across Nigeria. Four models are trained and evaluated: Logistic Regression widely accepted for its simplicity and interpretability. Random Forest is a robust ensemble learning algorithm capable of capturing complex relationships within the data. The decision Tree model is simple to understand and visualize and the Support Vector Machine model is known for producing a very good classification. Furthermore, the performance metrics uutilized to rate the predictive capabilities of the models are Accuracy, Precision, Sensitivity, F Score, and area under the Receiver Operating Characteristic (AUC & ROC Curve). Additionally, a features importance analysis is conducted for the identification of the dominant factors contributing to the prediction of the spread of medical insurance in Nigeria. The outcome of this paper gives insights in the efficiency of each machine learning models used to forecast medical insurance coverage, and identifying key determinants influencing insurance coverage can assist policymakers and healthcare stakeholders in devising targeted strategies to improve healthcare access and affordability for the Nigerian people.