A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

Colella Ylenia; De Lauri Chiara; Improta Giovanni; Rossano Lucia; Vecchione Donatella; Spinosa Tiziana; Giordano Vincenzo; Verdoliva Ciro; Santini Stefania; Colella Ylenia; De Lauri Chiara; Improta Giovanni; Rossano Lucia; Vecchione Donatella; Spinosa Tiziana; Giordano Vincenzo; Verdoliva Ciro; Santini Stefania

doi:10.3934/mbe.2021135

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 3: 2654-2674. doi: 10.3934/mbe.2021135

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A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

1.
Department of Electronic Engineering and Information Technology, Faculty of Engineering, University of Naples Federico II, Naples, Italy
2.
Department of Public Health of the University Hospital, University of Naples Federico II, Naples, Italy
3.
Interdepartmental Center for Research in Health Management and Innovation in Health (CIRMIS), University of Naples Federico II, Naples, Italy
4.
ASL Napoli 1 Centro, Naples, Italy

Received: 30 December 2020 Accepted: 04 March 2021 Published: 19 March 2021

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.
- Fuzzy Inference System,
- diabetes,
- health status,
- expert system,
- classification algorithms
Citation: Colella Ylenia, De Lauri Chiara, Improta Giovanni, Rossano Lucia, Vecchione Donatella, Spinosa Tiziana, Giordano Vincenzo, Verdoliva Ciro, Santini Stefania. A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2654-2674. doi: 10.3934/mbe.2021135

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Abstract

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.

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