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Care Cost Prediction Model for Orphanage Organizations in Saudi Arabia


Huda N Alhazmi, Alshymaa Alghamdi, Fatimah Alajlani, Samah Abuayied, and Fahd M Aldosari


Vol. 21  No. 4  pp. 84-92


Care services are a significant asset in human life. Care in its overall nature focuses on human needs and covers several aspects such as health care, homes, personal care, and education. In fact, care deals with many dimensions: physical, psychological, and social interconnections. Very little information is available on estimating the cost of care services that provided to orphans and abandoned children. Prediction of the cost of the care system delivered by governmental or non-governmental organizations to support orphans and abandoned children is increasingly needed. The purpose of this study is to analyze the care cost for orphanage organizations in Saudi Arabia to forecast the cost as well as explore the most influence factor on the cost. By using business analytic process that applied statistical and machine learning techniques, we proposed a model includes simple linear regression, Naive Bayes classifier, and Random Forest algorithms. The finding of our predictive model shows that Naive Bayes has addressed the highest accuracy equals to 87% in predicting the total care cost. Our model offers predictive approach in the perspective of business analytics.


Care cost, Orphanage organizations, Machine learning, Business analytic, Naive Bayes, Simple liner regression, Random Forest