Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.
 Portela, F., Santos, M. F., Machado, J., Abelha, A., Rua, F., & Silva, Á. (2015). Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients. In Ambient Intelligence for Health (pp. 77-90). Springer International Publishing.10.1007/978-3-319-26508-7_8Search in Google Scholar
 Abirami, N., Kamalakannan, T., & Muthukumaravel, A. (2013). A Study on Analysis of Various Data Mining Classification Techniques on Healthcare Data. International Journal of Emerging Technology and Advanced Engineering, 3(7), 604-607.Search in Google Scholar
 Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.Search in Google Scholar
 Reis, R., Mendonça, A., Ferreira, D. L. A., Peixoto, H.,&Machado, J. (2017). Business Intelligence for Nutrition Therapy. In Next- GenerationMobile and Pervasive Healthcare Solutions (pp. 203- 218). IGI Global.Search in Google Scholar
 Eapen, A. G. (2004). Application of Data mining in Medical Applications.Search in Google Scholar
 Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AImagazine, 17(3), 37.Search in Google Scholar
 Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.Search in Google Scholar
 Han, J., Kamber, M., 2001. Data Mining: Concepts and Techniques, Morgan Kaufmann, San Fco., CA., USA.Search in Google Scholar
 Shafique, U., & Qaiser, H. (2014). A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). Int. J. Innov. Sci. Res, 12(1), 217-222.Search in Google Scholar
 Milovic, B.,&Milovic, M. (2012). Prediction and decisionmaking in health care using data mining. Kuwait Chapter of the Arabian Journal of Business and Management Review, 1(12), 126.Search in Google Scholar
 Ferreira, P. M. S. (2010). Aplicação de Algoritmos de Aprendizagem Automática para a Previsão de Cancro de Mama (Master Thesis). Faculdade de Ciências da Universidade do Porto, Porto, Portugal.Search in Google Scholar
 Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233-240). ACM10.1145/1143844.1143874Search in Google Scholar
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