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Machine Learning Approaches to Estimating Health Insurance Expenses
Machine Learning Approaches to Estimating Health Insurance Expenses
Dr. Sheelesh Kumar Sharma , Md Naved Khan , Mohd Zaid Saifi , Mohd Sanif Khan
ABSTRACT
This study compares the performance of three machine learning models, XGBoost, Artificial Neural Networks (ANN), and Decision Trees, for a specific task.Provide some quantitative results or comparisons to support the claim that ANN performs the best among the three models. We also present a detailed analysis of the models, their strengths and weaknesses, and the factors that contribute to their performance. Our study contributes to the growing literature on machine learning and highlights the importance of selecting the appropriate model for a given task.
Based on the comparison of XGBoost, ANN, and Decision Tree models, it was found that the ANN model has out performance in the prediction task. The ANN model demonstrated a higher accuracy score and lower root mean squared error (MSE) compared to the other models. This indicates that the ANN model is more capable of accurately predicting the target variable compared to the other models. In addition to the comparison of the models, the study also explored the importance of feature selection and hyperparameter tuning in improving the performance of the models. The results showed that selecting relevant features and optimizing hyperparameters can significantly enhance the performance of the models.
Overall, the study highlights the potential of using ANN models in predictive tasks and emphasizes the importance of careful feature selection and hyperparameter tuning to achieve optimal performance
Keywords: machine learning models, XGBoost, Artificial Neural Networks, Decision Trees, performance, quantitative results, accuracy score, root mean squared error, feature selection, hyperparameter tuning, predictive tasks, optimal performance.
