Medical insurance claims are of organized crimes related to patients,
physicians, diagnostic centers, and insurance providers, forming a chain
reaction that must be monitored constantly. These kinds of frauds affect the
financial growth of both insured people and health insurance companies. The
Convolution Neural Network architecture is used to detect fraudulent claims
through a correlation study of regression models, which helps to detect money
laundering on different claims given by different providers. Supervised and
unsupervised classifiers are used to detect fraud and non-fraud claims.

医疗保险索赔存在与患者、医生、诊断中心和保险提供商相关的有组织犯罪，形成一种必须不断监测的连锁反应。通过对回归模型的相关性研究，使用卷积神经网络架构来检测欺诈性索赔，并帮助检测不同提供商提出的索赔中的洗钱行为。使用有监督和无监督分类器来检测欺诈性和非欺诈性的索赔。

深度学习神经网络相关的医疗索赔服务

Correlating Medi- Claim Service by Deep Learning Neural Networks

The study aimed to evaluate the regression models' performance in predicting
the cost of medical insurance. The Three (3) Regression Models in Machine
Learning namely Linear Regression, Gradient Boosting, and Support Vector
Machine were used. The performance will be evaluated using the metrics RMSE
(Root Mean Square), r2 (R Square), and K-Fold Cross-validation. The study also
sought to pinpoint the feature that would be most important in predicting the
cost of medical insurance.The study is anchored on the knowledge discovery in
databases (KDD) process. (KDD) process refers to the overall process of
discovering useful knowledge from data. It show the performance evaluation
results reveal that among the three (3) Regression models, Gradient boosting
received the highest r2 (R Square) 0.892 and the lowest RMSE (Root Mean Square)
1336.594. Furthermore, the 10-Fold Cross-validation weighted mean findings are
not significantly different from the r2 (R Square) results of the three (3)
regression models. In addition, Exploratory Data Analysis (EDA) using a box
plot of descriptive statistics observed that in the charges and smoker features
the median of one group lies outside of the box of the other group, so there is
a difference between the two groups. It concludes that Gradient boosting
appears to perform better among the three (3) regression models. K-Fold
Cross-Validation concluded that the three (3) regression models are good.
Moreover, Exploratory Data Analysis (EDA) using a box plot of descriptive
statistics ceases that the highest charges are due to the smoker feature.

本研究使用三种回归模型（线性回归，梯度提升，支持向量机）来预测医疗保险成本，并以 RMSE、R Square 和 K-Fold 交叉验证为评估指标。结果表明，在这些模型中，梯度提升的 R Square 最高 0.892，RMSE 最低 1336.594。研究总结认为，梯度提升模型在这三种模型中表现最好。同时，探索性数据分析显示，最高的费用是由于吸烟这一特征引起的。