By contrasting decision trees with logistic regression, a novel categorization-based cost prediction method for health insurance may be developed under supervision.

Abstract

AIM: The aim of research is to classify the cost prediction in health insurance using novel ranking with machine learning algorithms. Materials and Methods: The categorizing is performed by adopting  a sample size of n=10 in Decision Tree and sample size n = 10 in Logistic Regression  algorithms was iterated 20 times for efficient and accurate analysis on labeled images with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. Results: The analysis of  the results shows that the Decision  Tree has a high accuracy of (94.30%) in comparison with the Logistic Regression algorithm (92.70%). There is a statistically significant difference between the study groups with (p<0.05). Conclusion: Prediction in classifying the  cost prediction in health insurance shows that the Decision Tree appears to generate better accuracy than the cost prediction Logistic Regression algorithm.