Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes
In this work, a machine learning methodology is used to predict the progress of the glycemic values of six patients with diabetes. Eight different algorithms are compared i.e., ANN, PNN, Polynomial Regression, Gradient Boosted Trees Regression, Random Forest Regression, Simple Regression Tree, Tree...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Medical Sciences Forum |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9992/10/1/11 |
| _version_ | 1827725028995629056 |
|---|---|
| author | Alessandro Massaro Nicola Magaletti Gabriele Cosoli Angelo Leogrande Francesco Cannone |
| author_facet | Alessandro Massaro Nicola Magaletti Gabriele Cosoli Angelo Leogrande Francesco Cannone |
| author_sort | Alessandro Massaro |
| collection | DOAJ |
| description | In this work, a machine learning methodology is used to predict the progress of the glycemic values of six patients with diabetes. Eight different algorithms are compared i.e., ANN, PNN, Polynomial Regression, Gradient Boosted Trees Regression, Random Forest Regression, Simple Regression Tree, Tree Ensemble Regression, Linear Regression. The algorithms are classified based on the ability to minimize four statistical errors, namely: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Mean Signed Difference. Following the analysis, an ordering of the algorithms by predictive efficiency is proposed. Data are collected within the “<i>Smart District 4.0 Project</i>” with the contribution of the Italian Ministry of Economic Development. |
| first_indexed | 2024-03-10T22:22:45Z |
| format | Article |
| id | doaj.art-f2eda85472814ff9b8890855686852c3 |
| institution | Directory Open Access Journal |
| issn | 2673-9992 |
| language | English |
| last_indexed | 2024-03-10T22:22:45Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medical Sciences Forum |
| spelling | doaj.art-f2eda85472814ff9b8890855686852c32023-11-19T12:13:20ZengMDPI AGMedical Sciences Forum2673-99922022-02-011011110.3390/IECH2022-12293Use of Machine Learning to Predict the Glycemic Status of Patients with DiabetesAlessandro Massaro0Nicola Magaletti1Gabriele Cosoli2Angelo Leogrande3Francesco Cannone4Dipartimento di Management, Finanza e Tecnologia, LUM-Libera Università Mediterranea “Giuseppe Degennaro”, 70010 Bari, ItalyLUM Enterprise S.r.l., 70010 Bari, ItalyLUM Enterprise S.r.l., 70010 Bari, ItalyLUM Enterprise S.r.l., 70010 Bari, ItalyEmtesys S.r.l., 70122 Bari, ItalyIn this work, a machine learning methodology is used to predict the progress of the glycemic values of six patients with diabetes. Eight different algorithms are compared i.e., ANN, PNN, Polynomial Regression, Gradient Boosted Trees Regression, Random Forest Regression, Simple Regression Tree, Tree Ensemble Regression, Linear Regression. The algorithms are classified based on the ability to minimize four statistical errors, namely: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Mean Signed Difference. Following the analysis, an ordering of the algorithms by predictive efficiency is proposed. Data are collected within the “<i>Smart District 4.0 Project</i>” with the contribution of the Italian Ministry of Economic Development.https://www.mdpi.com/2673-9992/10/1/11machine learningpredictionstelemedicineANN-artificial neural network |
| spellingShingle | Alessandro Massaro Nicola Magaletti Gabriele Cosoli Angelo Leogrande Francesco Cannone Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes Medical Sciences Forum machine learning predictions telemedicine ANN-artificial neural network |
| title | Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes |
| title_full | Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes |
| title_fullStr | Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes |
| title_full_unstemmed | Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes |
| title_short | Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes |
| title_sort | use of machine learning to predict the glycemic status of patients with diabetes |
| topic | machine learning predictions telemedicine ANN-artificial neural network |
| url | https://www.mdpi.com/2673-9992/10/1/11 |
| work_keys_str_mv | AT alessandromassaro useofmachinelearningtopredicttheglycemicstatusofpatientswithdiabetes AT nicolamagaletti useofmachinelearningtopredicttheglycemicstatusofpatientswithdiabetes AT gabrielecosoli useofmachinelearningtopredicttheglycemicstatusofpatientswithdiabetes AT angeloleogrande useofmachinelearningtopredicttheglycemicstatusofpatientswithdiabetes AT francescocannone useofmachinelearningtopredicttheglycemicstatusofpatientswithdiabetes |