An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia DOI Creative Commons

Obaid Algahtani,

Mohammed M. A. Almazah,

Farouq Alshormani

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

As per the World Health Organization (WHO), justifications of people with disabilities globally are restricted by physical and social barriers that exclude their full contribution to society. Constructed environment can limit availability transportation, employment, goods services, healthcare, overall independent drive. The government Saudi Arabia has applied programs policies enhance quality life for disabilities, including education, employment chances. Furthermore, they also take action progress a few guards endorse public involvement income-support plans individuals besides efforts uphold cultural, social, political, economic accurate plans. Therefore, this study presents Effectiveness Machine Learning Models estimating Financial Cost Assistive Services Disability Care (EMLM-EFCASDC) technique in KSA. presented EMLM-EFCASDC mainly aims develop data-driven model accurately predicts cost assistive services disability care across At first, approach utilizes Z-score normalization preprocess input data, ensuring data variability is minimized improved accuracy. Next, an ensemble machine learning (ML) models comprises three classifiers such as hybrid kernel extreme (HKELM), gradient boosting (XGBoost), support vector regression (SVR) predicting financial cost. Eventually, modified pelican optimization algorithm (MPOA) utilized fine-tune optimal hyperparameter parameters achieve high predictive performance. An extensive range simulation analyses employed ensure enhanced performance technique. validation method portrayed least RMSLE value 0.1154 on existing approaches terms diverse evaluation measures.

Language: Английский

An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia DOI Creative Commons

Obaid Algahtani,

Mohammed M. A. Almazah,

Farouq Alshormani

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 28, 2025

As per the World Health Organization (WHO), justifications of people with disabilities globally are restricted by physical and social barriers that exclude their full contribution to society. Constructed environment can limit availability transportation, employment, goods services, healthcare, overall independent drive. The government Saudi Arabia has applied programs policies enhance quality life for disabilities, including education, employment chances. Furthermore, they also take action progress a few guards endorse public involvement income-support plans individuals besides efforts uphold cultural, social, political, economic accurate plans. Therefore, this study presents Effectiveness Machine Learning Models estimating Financial Cost Assistive Services Disability Care (EMLM-EFCASDC) technique in KSA. presented EMLM-EFCASDC mainly aims develop data-driven model accurately predicts cost assistive services disability care across At first, approach utilizes Z-score normalization preprocess input data, ensuring data variability is minimized improved accuracy. Next, an ensemble machine learning (ML) models comprises three classifiers such as hybrid kernel extreme (HKELM), gradient boosting (XGBoost), support vector regression (SVR) predicting financial cost. Eventually, modified pelican optimization algorithm (MPOA) utilized fine-tune optimal hyperparameter parameters achieve high predictive performance. An extensive range simulation analyses employed ensure enhanced performance technique. validation method portrayed least RMSLE value 0.1154 on existing approaches terms diverse evaluation measures.

Language: Английский

Citations

0