A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions

Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for anal...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 22; H. 22; S. 8615
Hauptverfasser:	Mavrogiorgou, Argyro, Kiourtis, Athanasios, Kleftakis, Spyridon, Mavrogiorgos, Konstantinos, Zafeiropoulos, Nikolaos, Kyriazis, Dimosthenis
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.11.2022 MDPI
Schlagworte:	Accident prevention Algorithms Artificial intelligence Bayes Theorem Breast cancer Cardiovascular disease catalogue Computational linguistics COVID-19 Data analysis Data mining Datasets Decision making Decision trees Delivery of Health Care Development and progression healthcare Heart Humans Information management Kidney diseases Language processing Machine Learning Medical research Medicine, Experimental Natural language interfaces Neural networks Patients prediction Route optimization Smart cities Stroke supervised learning Unmanned aerial vehicles prediction data analysis machine learning supervised learning catalogue healthcare
ISSN:	1424-8220, 1424-8220
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Abstract	Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain’s requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario’s requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios’ nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism’s efficiency.
AbstractList	Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency. Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain's requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario's requirements and datasets, for efficiently predicting the onset of a disease. To this context, seven (7) different ML algorithms (Naïve Bayes, K-Nearest Neighbors, Decision Tree, Logistic Regression, Random Forest, Neural Networks, Stochastic Gradient Descent) have been executed on top of diverse healthcare scenarios (stroke, COVID-19, diabetes, breast cancer, kidney disease, heart failure). Based on a variety of performance metrics (accuracy, recall, precision, F1-score, specificity, confusion matrix), it has been identified that a sub-set of ML algorithms are more efficient for timely predictions under specific healthcare scenarios, and that is why the envisioned ML catalogue prioritizes the ML algorithms to be used, depending on the scenarios' nature and needed metrics. Further evaluation must be performed considering additional scenarios, involving state-of-the-art techniques (e.g., cloud deployment, federated ML) for improving the mechanism's efficiency.
Audience	Academic
Author	Zafeiropoulos, Nikolaos Mavrogiorgou, Argyro Kleftakis, Spyridon Kyriazis, Dimosthenis Kiourtis, Athanasios Mavrogiorgos, Konstantinos
AuthorAffiliation	Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece
AuthorAffiliation_xml	– name: Department of Digital Systems, University of Piraeus, 185 34 Piraeus, Greece
Author_xml	– sequence: 1 givenname: Argyro orcidid: 0000-0002-1543-5627 surname: Mavrogiorgou fullname: Mavrogiorgou, Argyro – sequence: 2 givenname: Athanasios orcidid: 0000-0002-1681-3626 surname: Kiourtis fullname: Kiourtis, Athanasios – sequence: 3 givenname: Spyridon orcidid: 0000-0002-6237-488X surname: Kleftakis fullname: Kleftakis, Spyridon – sequence: 4 givenname: Konstantinos surname: Mavrogiorgos fullname: Mavrogiorgos, Konstantinos – sequence: 5 givenname: Nikolaos surname: Zafeiropoulos fullname: Zafeiropoulos, Nikolaos – sequence: 6 givenname: Dimosthenis orcidid: 0000-0001-7019-7214 surname: Kyriazis fullname: Kyriazis, Dimosthenis
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36433212$$D View this record in MEDLINE/PubMed
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Keywords	prediction data analysis machine learning supervised learning catalogue healthcare
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This manuscript is an extended version of conference paper A Comparative Study of ML Algorithms for Scenario-Agnostic Predictions in Healthcare. In Proceedings of the ICTS4eHealth 2022, Rhodes Island, Greece, 30 June–3 July 2022.
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Snippet	Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a...
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SubjectTerms	Accident prevention Algorithms Artificial intelligence Bayes Theorem Breast cancer Cardiovascular disease catalogue Computational linguistics COVID-19 Data analysis Data mining Datasets Decision making Decision trees Delivery of Health Care Development and progression healthcare Heart Humans Information management Kidney diseases Language processing Machine Learning Medical research Medicine, Experimental Natural language interfaces Neural networks Patients prediction Route optimization Smart cities Stroke supervised learning Unmanned aerial vehicles
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Title	A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions
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