With the exponential growth of the Internet of Things and Cloud Computing, especially in recent years, the potentiality of Machine Learning (ML) has been demonstrated and amplified, together with data mining developments and the availability of large amounts of data. In order to design a ML system capable of producing effective and accurate predictions and results it is necessary to make sure that it is actually working on large data sets; clean, high quality and complete data really representing the information you are trying to analyze. More data are available, the more accurate are evidently predictions. Forecasting represents an important use of extracted knowledge from data. It is the process of predict future demand for an offered product or service, and it allows for optimizing company decisions, reducing risks, managing stocks, planning sales and making many other internal or in-market assessments. In this paper we present and discuss a novel ensemble technique in forecasting workload of local health department. The proposed approach relies on a real dataset composed by over than 20 M of administrative e-health records. Obtained results demonstrate that our ensemble approach outperforms the state-of-the-art.

A robust ensemble technique in forecasting workload of local healthcare departments

Piccialli F.
Primo
;
Giampaolo F.;Cuomo S.
2021

Abstract

With the exponential growth of the Internet of Things and Cloud Computing, especially in recent years, the potentiality of Machine Learning (ML) has been demonstrated and amplified, together with data mining developments and the availability of large amounts of data. In order to design a ML system capable of producing effective and accurate predictions and results it is necessary to make sure that it is actually working on large data sets; clean, high quality and complete data really representing the information you are trying to analyze. More data are available, the more accurate are evidently predictions. Forecasting represents an important use of extracted knowledge from data. It is the process of predict future demand for an offered product or service, and it allows for optimizing company decisions, reducing risks, managing stocks, planning sales and making many other internal or in-market assessments. In this paper we present and discuss a novel ensemble technique in forecasting workload of local health department. The proposed approach relies on a real dataset composed by over than 20 M of administrative e-health records. Obtained results demonstrate that our ensemble approach outperforms the state-of-the-art.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/856938
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