Linear regression analysis achieved a maximum accuracy of 97.89% in forecasting overall healthcare costs compared to naive Bayes and random forest algorithms.
Which machine learning algorithm provides the highest accuracy in predicting overall healthcare costs based on BMI and patient records?
Linear regression models can highly accurately predict overall healthcare costs using patient BMI and diagnostic records.
Medical costs are one of the most common recurring expenses in a person's life. Based on different research studies, BMI, ageing, smoking, and other factors are all related to greater personal medical care costs. The estimates of the expenditures of health care related to obesity are needed to help create cost-effective obesity prevention strategies. Obesity prevention at a young age is a top concern in global health, clinical practice, and public health. To avoid these restrictions, genetic variants are employed as instrumental variables in this research. Using statistics from public huge datasets, the impact of body mass index (BMI) on overall healthcare expenses is predicted. A multiview learning architecture can be used to leverage BMI information in records, including diagnostic texts, diagnostic IDs, and patient traits. A hierarchy perception structure was suggested to choose significant words, health checks, and diagnoses for training phase informative data representations, because various words, diagnoses, and previous health care have varying significance for expense calculation. In this system model, linear regression analysis, naive Bayes classifier, and random forest algorithms were compared using a business analytic method that applied statistical and machine-learning approaches. According to the results of our forecasting method, linear regression has the maximum accuracy of 97.89 percent in forecasting overall healthcare costs. In terms of financial statistics, our methodology provides a predictive method.
Taloba et al. (Wed,) conducted a other in Healthcare expenses related to BMI and obesity. Linear regression analysis vs. Naive Bayes classifier and random forest algorithms was evaluated on Accuracy in forecasting overall healthcare costs. Linear regression analysis achieved a maximum accuracy of 97.89% in forecasting overall healthcare costs compared to naive Bayes and random forest algorithms.