International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Forecasting the Amount of Medicaid Claims using Machine Learning Techniques Nandita Vivek Suryawanshi1 1BE Computer Engineering, K. K. Wagh Institute of Engineering Education and Research, Nashik, Maharashtra.
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Health insurance claim prediction is a critical task 2. Literature Review for insurance companies as it allows them to estimate future claims, manage risk, and set premiums more effectively. Traditional actuarial methods have been employed for decades, but the advent of machine learning has opened up new opportunities to improve the accuracy and efficiency of claim predictions. This paper investigates various machine learning algorithms, including linear regression, decision trees, random forests, and neural networks, to predict health insurance claim amounts. The study compares the performance of these models using key evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results indicate that machine learning methods can significantly outperform traditional models, providing insurers with more accurate tools for managing claims and optimizing business strategies.
2.1 Traditional Actuarial Methods Traditional methods for insurance claim prediction are largely based on statistical models, such as generalized linear models (GLMs) and Poisson regression. While these models are interpretable and easy to implement, they often struggle to model complex nonlinear interactions in the data. Studies have shown that these limitations can lead to less accurate predictions, particularly when the data includes numerous covariates and nonlinear patterns. 2.2 Machine Learning in Insurance In recent years, machine learning techniques have been increasingly applied to insurance data. These techniques include decision trees, random forests, support vector machines (SVMs), and deep neural networks (DNNs). Studies have shown that ML models can outperform traditional models in terms of predictive accuracy because they can capture complex relationships and adapt to nonlinearity in the data. These techniques, however, come at the cost of reduced interpretability compared to traditional models.
Key Words: Insurance claims prediction, machine learning, neural networks, decision trees, random forests, and linear regression.
1.INTRODUCTION Health insurance companies face considerable challenges in predicting future claims accurately, which is essential for setting appropriate premiums, managing risk, and ensuring profitability. Traditionally, actuarial techniques have been employed to model and predict claim amounts based on historical data. However, these methods may struggle to capture complex relationships in the data, such as nonlinear patterns and interactions between multiple variables.
3. Methodology 3.1 Data Collection and Preprocessing The dataset used in this study contains historical health insurance claim records, which include variables such as age, gender, smoking status, body mass index (BMI), and geographical region. The target variable is the claim amount.
Machine learning (ML) algorithms have shown immense potential in various domains, including finance, medicine, and insurance. These models can handle large datasets, capture complex relationships, and adapt to changes in the underlying patterns of the data. This paper aims to explore the efficacy of several machine learning models for predicting health insurance claim amounts, comparing their performance against traditional methods and discussing the practical implications for insurers.
Data preprocessing steps include handling missing values, encoding categorical variables (e.g., gender, region), and normalizing continuous features (e.g., BMI, age). Outliers are also detected and removed to improve model performance. 3.2 Models of machine learning A number of machine learning algorithms are used to anticipate claim amounts.
Forecasting the amount of Medicaid claims is crucial for government agencies, healthcare providers, and insurers to manage resources efficiently. By using machine learning (ML) techniques, stakeholders can leverage historical data, patient demographics, and economic trends to predict the volume and cost of claims, enabling better planning, fraud detection, and financial sustainability.
© 2024, IRJET
|
Impact Factor value: 8.315
A basic predictive model called "linear regression" posits a linear relationship between the independent variables and the dependent variable, or claim amount.
|
ISO 9001:2008 Certified Journal
|
Page 579