Abstract
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.
Highlights
Based on different research studies, body mass index (BMI), ageing, smoking, and other factors are all related to greater personal medical care costs. e estimates of the expenditures of health care related to obesity are needed to help create cost-effective obesity prevention strategies
If regression used one independent variable, it is known as univariate regression analysis, or else if it used more than two independent variables it is known as multivariate regression analysis
E mean absolute error, is ineffective for comparing outcomes with costs stated in various dollars, so we will use the mean absolute percentage error (MAPE), a customized absolute error in which the MAE is reduced by the mean cost and calculated as follows: MAPE (1/n) ni 1 |yi − yi|. (7) m
Summary
Some of the recent literature that describes the various mechanism of estimating the costs of physical healthcare is summarized below. Using specialist characteristics and situational integration of medical knowledge provides a cost-sensitive implementation of the long shortterm memory (LSTM) neural net Using both machinederived and professional characteristics, including frequent patterns, and resolving the issue of class imbalances, this research focuses on important parts of an EHR-driven forecasting system in a single framework. To assess the economic benefit of complementing claim-based forecasting analytics with electronic medical record (EMR)derived data and to contrast machine-learning techniques to conventional logistic regression in forecasting critical results in patients with HF, healthcare patients with HF from 2 healthcare professional systems in Massachusetts, Boston, were included in predictive research with a one-year followup duration. E goal of this study was to evaluate the effectiveness of machine-learning methodologies for predicting healthcare expenses connected with spinal fusion in aspects of gains or losses in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to use these techniques to investigate the major features connected with spinal fusion medical costs. Because the system can adjust to changes in admission time and cohort size while requiring no additional manual coding, it has the potential to aid in cost estimation for active patients and enable improved functional outcome in hospitals
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