Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty

Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases....

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Veröffentlicht in:Bioengineering (Basel) Jg. 12; H. 2; S. 150
Hauptverfasser: Vrouva, Sotiria, Koumantakis, George A., Sopidou, Varvara, Tatsios, Petros I., Raptis, Christos, Adamopoulos, Adam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.02.2025
MDPI
Schlagworte:
Accuracy
Algorithms
Arthroplasty
Artificial intelligence
Classification
Cluster analysis
Clustering
Comparative analysis
Computer applications
Data mining
Datasets
Genetic algorithms
Genomes
hybrid machine learning algorithms
Intelligence
Joint replacement surgery
Learning algorithms
Machine learning
Optimization
Orthopedics
Patient satisfaction
Physical therapy
Prognosis
Rehabilitation
reverse total shoulder arthroplasty prognosis
Shoulder
Test sets
Therapeutics, Physiological
Vector quantization
Greece
Thrace
hybrid machine learning algorithms
reverse total shoulder arthroplasty prognosis
genetic algorithms
ISSN:2306-5354, 2306-5354
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Abstract Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms’ classification accuracy and patients’ rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients’ rehabilitation prognosis.
AbstractList Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms’ classification accuracy and patients’ rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients’ rehabilitation prognosis.
Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms' classification accuracy and patients' rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients' rehabilitation prognosis.Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms' classification accuracy and patients' rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients' rehabilitation prognosis.
Audience Academic
Author Adamopoulos, Adam
Tatsios, Petros I.
Raptis, Christos
Vrouva, Sotiria
Koumantakis, George A.
Sopidou, Varvara
AuthorAffiliation 2 Department of Physical Therapy, 401 Army General Hospital of Athens, 11525 Athens, Greece
4 Department of Biomedical Sciences, School of Health and Care Sciences, University of West Attica (UNIWA), 12243 Athens, Greece; vsopidou@uniwa.gr
1 Physiotherapy Department, School of Health and Care Sciences, University of West Attica (UNIWA), 12243 Athens, Greece; gkoumantakis@uniwa.gr (G.A.K.); ptatsios@uniwa.gr (P.I.T.)
3 Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; craptis@med.duth.gr (C.R.); adam@med.duth.gr (A.A.)
AuthorAffiliation_xml – name: 1 Physiotherapy Department, School of Health and Care Sciences, University of West Attica (UNIWA), 12243 Athens, Greece; gkoumantakis@uniwa.gr (G.A.K.); ptatsios@uniwa.gr (P.I.T.)
– name: 2 Department of Physical Therapy, 401 Army General Hospital of Athens, 11525 Athens, Greece
– name: 4 Department of Biomedical Sciences, School of Health and Care Sciences, University of West Attica (UNIWA), 12243 Athens, Greece; vsopidou@uniwa.gr
– name: 3 Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; craptis@med.duth.gr (C.R.); adam@med.duth.gr (A.A.)
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/40001670$$D View this record in MEDLINE/PubMed
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Keywords hybrid machine learning algorithms
reverse total shoulder arthroplasty prognosis
genetic algorithms
Language English
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SubjectTerms Accuracy
Algorithms
Arthroplasty
Artificial intelligence
Classification
Cluster analysis
Clustering
Comparative analysis
Computer applications
Data mining
Datasets
Genetic algorithms
Genomes
hybrid machine learning algorithms
Intelligence
Joint replacement surgery
Learning algorithms
Machine learning
Optimization
Orthopedics
Patient satisfaction
Physical therapy
Prognosis
Rehabilitation
reverse total shoulder arthroplasty prognosis
Shoulder
Test sets
Therapeutics, Physiological
Vector quantization
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Title Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty
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