Using gait analysis’ parameters to classify Parkinsonism: A data mining approach

Ricciardi, Carlo; Amboni, Marianna; Santis, Chiara De; Improta, Giovanni; Volpe, Giovanni; Iuppariello, Luigi; Ricciardelli, Gianluca; D’Addio, Giovanni; Vitale, Carmine; Barone, Paolo; Cesarelli, Mario

doi:10.1016/j.cmpb.2019.105033

Cited by 62 publications

(28 citation statements)

References 37 publications

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“…Tree-based algorithms are empowerments of a simpler decision tree that can make it stronger and let it achieve higher accuracy in the prediction tasks 36 – 38 . They belong to the so-called supervised learning, which consists in making a classifier learn from the data by providing it with the classes of each subject.…”

Section: Methodsmentioning

confidence: 99%

“…Second, our ML analysis was fully addressed to a tree-based approach. While other classifiers can be employed, a fully tree-based approach and, in general, decision tree-based algorithms have already shown in literature their great potential 36 – 38 . Third, despite having performed a validation internally through the cross-validation, the models were not externally validated in an independent dataset and thus overfitting cannot be ruled out.…”

Section: Limitationmentioning

confidence: 99%

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Machine learning to predict mortality after rehabilitation among patients with severe stroke

Scrutinio

Ricciardi

Donisi

et al. 2020

Sci Rep

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Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.

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Section: Methodsmentioning

confidence: 99%

Section: Limitationmentioning

confidence: 99%

Machine learning to predict mortality after rehabilitation among patients with severe stroke

Scrutinio

Ricciardi

Donisi

et al. 2020

Sci Rep

Self Cite

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“…Conversely, Motwani et al 35 investigated the feasibility and accuracy of machine learning to predict 5-year all-cause mortality in patients undergoing coronary computed tomographic angiography (CCTA) and compared the performances to the existing clinical or CCTA metrics. Some classification procedures have been proposed by researchers that compare different techniques [36][37][38][39] or assess cardiovascular risk based on machine learning. [40][41][42] Another study investigated heart valve disease with the adaptive neuro-fuzzy inference system.…”

Section: Related Work and Aimmentioning

confidence: 99%

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Ricciardi

Valente

Edmund

et al. 2020

Health Informatics J

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Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

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“…This opens a debate about how to draw meaning from this exponentially growing amount of data. The analysis of such data is important for extracting information, gaining knowledge, and discovering hidden patterns [1][2][3]. Additionally, analysis of health big data improves the quality of services and reduces costs [4].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Adoption of Big Data Management in Healthcare in Jordan

Bani-Salameh

Al-Qawaqneh

Taamneh

2021

Data

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Software developers and data scientists use and deal with big data to easily discover useful knowledge and find better solutions to improve healthcare services and patient safety. Big data analytics (BDA) is getting attention due to its role in decision-making across the healthcare field. Therefore, this article examines the adoption mechanism of big data analytics and management in healthcare organizations in Jordan. Additionally, it discusses health big data’s characteristics and the challenges, and limitations for health big data analytics and management in Jordan. This article proposes a conceptual framework that allows utilizing health big data. The proposed conceptual framework suggests a way to merge the existing health information system with the National Health Information Exchange (HIE), which might play a role in extracting insights from our massive datasets, increases the data availability and reduces waste in resources. When applying the framework, the collected data are processed to develop knowledge and support decision-making, which helps improve the health care quality for both the community and individuals by improving diagnosis, treatment, and other services.

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Using gait analysis’ parameters to classify Parkinsonism: A data mining approach

Cited by 62 publications

References 37 publications

Machine learning to predict mortality after rehabilitation among patients with severe stroke

Machine learning to predict mortality after rehabilitation among patients with severe stroke

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Investigating the Adoption of Big Data Management in Healthcare in Jordan

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