Life Expectancy Prediction through Analysis of Immunization and HDI Factors using Machine Learning Regression Algorithms

Lakshmanarao, A.; Srisaila, A; T, Srinivasa Ravi Kiran; Lalitha, G; K, Vasanth Kumar

doi:10.3991/ijoe.v18i13.33315

Cited by 5 publications

(2 citation statements)

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“…They have proposed a Random Forest algorithm which is used to predict the life expectancy by considering factors of WHO dataset, demographic factors, socio economic distribution, Death rates, Immunization and HDI factors. As they have distinguished between the Support Vector Machine, Decision tree, Logistic Regression the Random Forest Regression model has Achieved higher accuracy [5].…”

Section: Literature Surveymentioning

confidence: 99%

Predictive Analytics for Future Life Expectancy Using Machine Learning

Vijaya

2024

IJSREM

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Advanced machine learning is used in the project "Predictive Analytics for Future Life Expectancy Using Machine Learning." methods for forecasting forthcoming trends in lifespan. By examining a range of datasets, including historical lifetime records, healthcare data, economic features, demographics, and environmental information, the research develops accurate prediction models. To assure reliability, these models—which were constructed using a variety of machine learning algorithms, including ensemble approaches, decision trees, and regression—go through a thorough training and validation process. This resulting forecasting tool is able to be used to legislators, healthcare professionals, and academics to make informed decisions on public health, including resource allocation. Periodic updates and monitoring enable reaction to evolving trends all while maintaining relevance and effectiveness over time. Key Words: Predictive Analytics, Future Life Expectancy, Linear regressor, Decision Tree regressor, Random forest regressor, Xgboost.

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Section: Literature Surveymentioning

confidence: 99%

Predictive Analytics for Future Life Expectancy Using Machine Learning

Vijaya

2024

IJSREM

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“…Predicting the insurance premium charge may help governments forecast the price to help them decide on healthrelated issues [24], [25]. The study compared the performance of four regression models: multiple linear regression, decision tree regression, support vector regression, and random forest regression.…”

Section: ) Regression Algorithmsmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Health Insurance Pricing

Bau,

Md Hanif

2024

JOIV : Int. J. Inform. Visualization

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Insurance is an effective way to guard against potential loss. Risk management is primarily employed to protect against the risk of a financial loss. Risk and uncertainty are inevitable parts of life, and the pace of life has led to a rise in these risks and uncertainties. Health insurance pricing has emerged as one of the essential fields of this study following the coronavirus pandemic. The anticipated outcomes from this study will be applied to guarantee that an insurance company's goal for its health insurance packages is within the range of profitability so that the insurance company will also choose the most price-effective course of action. The US Health Insurance dataset was utilized for this study. This health insurance pricing prediction aims to examine four different types of regression-based machine learning algorithms: multiple linear regression, ridge regression, XGBoost regression, and random forest regression. The implemented model's performance is assessed using four evaluation metrics: MAE, MSE, RMSE, and R2 score. Random forest regression outperforms all other algorithms in terms of all four evaluation metrics. The best machine learning algorithm, random forest, is further enhanced with hyperparameter tuning. Random forest with hyperparameter tuning performs better for three evaluation metrics except for MAE. To gain further insights, data visualizations are also implemented to showcase the importance of features and the differences between actual and predicted prices for all the data points.

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