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Iranian Association of Medical InformaticsFrontiers in Health Informatics2676-710410120211016Designing a Profit and Loss Prediction Model for Health Companies Using Data Mining30530510.30699/fhi.v10i1.305ENAliAbdolahiSama Technical and Vocational Training College, Islamic Azad University, Bandar Mahshahr Branch, Bandar Mahshahr,. abdolahi1113@gmail.comValiNowzariAliPirzadSeyed EhsanAmirhosseini202105102021101320210902Introduction: Health companies need investment for development. Due to the high risk of their activities, it is very difficult to attract investment for this field, but this lack of financial resources leads to the failure of these companies, so providing a model for predicting profits and losses in companies is very important and functional.Materials and Method: In this study, a combination of two logistic regression algorithms and differential analysis were used to design a profit and loss forecasting model. Also, the information of 20 companies in the field of health was used to evaluate the proposed model. 10 profitable companies and 10 loss-making companies were selected and for each company, nine variables independent of the financial information of these companies were collected.Results: The designed prediction model was implemented on the data in this study. To do this, the data were divided into two sets: training and testing. The prediction model was implemented on training data and evaluated by test data and reached 99.65% sensitivity, 94.75% specificity and 96.28% accuracy. The proposed model was then compared with the methods of decision tree C4.5, Bayesian, support vector machine, nearest neighborhood and multilayer neural network and it was found to have a better output.Conclusion: In this study, it was found that the risk in the field of health investment can be reduced, so the profit and loss situation of health companies can be predicted with appropriate accuracy. It was also found that the combination of logistic regression and differential analysis algorithms can increase the accuracy of the prediction model.


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