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Estimation and Prediction of Hospitalization and Medical Care Costs Using Regression in Machine Learning.

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This study predicts healthcare costs using body mass index (BMI) and patient data. Linear regression achieved 97.89% accuracy, offering a valuable predictive method for healthcare expenses.

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Area of Science:

  • Health Economics
  • Medical Informatics
  • Biostatistics

Background:

  • Rising healthcare costs are a significant concern, influenced by factors like body mass index (BMI), aging, and smoking.
  • Obesity prevention, particularly in younger populations, is a critical global health priority.
  • Accurate estimation of obesity-related healthcare expenditures is essential for developing effective prevention strategies.

Purpose of the Study:

  • To predict the impact of body mass index (BMI) on overall healthcare expenses using large public datasets.
  • To leverage multiview learning architectures to integrate BMI information from diverse patient records, including diagnostic texts, IDs, and traits.
  • To develop a predictive methodology for healthcare costs, enhancing financial statistics and informing public health policy.

Main Methods:

  • Utilized genetic variants as instrumental variables to overcome research limitations.
  • Employed a hierarchy perception structure to identify significant features (words, health checks, diagnoses) for data representation.
  • Compared linear regression analysis, naive Bayes classifier, and random forest algorithms using statistical and machine-learning approaches.

Main Results:

  • Linear regression analysis demonstrated the highest accuracy, achieving 97.89% in forecasting overall healthcare costs.
  • The multiview learning architecture effectively leveraged patient data, including diagnostic information and traits, for BMI-related cost prediction.
  • The proposed methodology provides a robust predictive method for financial statistics in healthcare.

Conclusions:

  • The study successfully developed a predictive model for healthcare costs based on BMI and patient data.
  • Linear regression emerged as the most accurate algorithm for forecasting healthcare expenditures.
  • The findings offer a valuable tool for cost-effective obesity prevention strategies and healthcare financial planning.