Linear and Ensembling Regression Based Health Cost Insurance Prediction Using Machine Learning

Devi, M. Shyamala; Swathi, P.; Reddy, M. Purushotham; Varma, VamsiK.; Reddy, A. Praveen Kumar; Vivekanandan, S.; Moorthy, Priyanka

doi:10.1007/978-981-16-1502-3_49

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…All models were evaluated using semi-possible generalized linear models with variations such as record link and Poisson. At the beginning of each year, annual expenses are estimated in subgroups defined by alcohol consumption, socioeconomic status, smoking level, educational qualifications, and strenuous exercise in recruitment [ 37 ]. The diversity of the proportional increases in annual costs among the types of each subgroup was estimated using the chi-square test.…”

Section: Results and Analysismentioning

confidence: 99%

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

Taloba

El-Aziz

Alshanbari

et al. 2022

Journal of Healthcare Engineering

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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.

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Section: Results and Analysismentioning

confidence: 99%

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

Taloba

El-Aziz

Alshanbari

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Because a medical problem can strike anybody at any moment and have such a significant psychological and economic impact on the individual, it is difficult to predict when one will occur. With this background in mind, this research [20] aimed to forecast the cost of health insurance using the following contributions: age, gender, region, smoking, BMI, and children.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Regression Framework to Predict Health Insurance Premiums

Kaushik

Bhardwaj

Dwivedi

et al. 2022

IJERPH

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Artificial intelligence (AI) and machine learning (ML) in healthcare are approaches to make people’s lives easier by anticipating and diagnosing diseases more swiftly than most medical experts. There is a direct link between the insurer and the policyholder when the distance between an insurance business and the consumer is reduced to zero with the use of technology, especially digital health insurance. In comparison with traditional insurance, AI and machine learning have altered the way insurers create health insurance policies and helped consumers receive services faster. Insurance businesses use ML to provide clients with accurate, quick, and efficient health insurance coverage. This research trained and evaluated an artificial intelligence network-based regression-based model to predict health insurance premiums. The authors predicted the health insurance cost incurred by individuals on the basis of their features. On the basis of various parameters, such as age, gender, body mass index, number of children, smoking habits, and geolocation, an artificial neural network model was trained and evaluated. The experimental results displayed an accuracy of 92.72%, and the authors analyzed the model’s performance using key performance metrics.

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Prediction of Cost for Medical Care Insurance by Using Regression Models

Sandra,

Joshi,

Ravi

et al. 2024

Lecture Notes in Electrical Engineering

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Linear and Ensembling Regression Based Health Cost Insurance Prediction Using Machine Learning

Cited by 8 publications

References 9 publications

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

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

Machine Learning-Based Regression Framework to Predict Health Insurance Premiums

Prediction of Cost for Medical Care Insurance by Using Regression Models

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