Classifying Different Stages of Parkinson’s Disease Through Random Forests

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

doi:10.1007/978-3-030-31635-8_140

Cited by 27 publications

(14 citation statements)

References 25 publications

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“…59 It has been used in different research applications, such as in neurology, radiology, eye-related pathologies, and cardiotocography. 60–63…”

Section: Methodsmentioning

confidence: 99%

“…59 It has been used in different research applications, such as in neurology, radiology, eye-related pathologies, and cardiotocography. [60][61][62][63] PCA PCA is a linear dimensionality reduction technique that can be used to reduce a large set of variables to a smaller set that still contains most of the original information. 64 It seeks a linear combination of variables such that the maximum variance can be extracted.…”

Section: Toolsmentioning

confidence: 99%

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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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“…59 It has been used in different research applications, such as in neurology, radiology, eye-related pathologies, and cardiotocography. 60–63…”

Section: Methodsmentioning

confidence: 99%

Section: Toolsmentioning

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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“…Mathematical and managerial tools strategies can help in reducing costs and in improving quality of healthcare services, from multi-criteria decision-making methods (Improta et al, 2019a, b;Ricciardi et al, 2020a;Sorrentino et al, 2020;Trunfio et al, 2020), to algorithms for biomedical data mining (Ricciardi et al, 2019a(Ricciardi et al, , 2020bD'Addio et al, 2020;Romeo et al, 2020) and analysis (Romeo et al, 2014(Romeo et al, , 2018Ricciardi et al, 2020c;Improta et al, 2020a), to novel process management and re-engineering techniques (Improta et al, 2019c;Montella et al, 2017;Converso et al, 2015;Cesarelli et al, 2020). Among the most common managerial techniques, the lean Six Sigma (LSS) methodology plays a considerable role (Improta et al, 2017(Improta et al, , 2018a.…”

Section: Introductionmentioning

confidence: 99%

Implementing fast track surgery in hip and knee arthroplasty using the lean Six Sigma methodology

Latessa

Fiorillo

Picone

et al. 2021

TQM

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PurposeOne of the biggest challenges in the health sector is that of costs compared to economic resources and the quality of services. Hospitals register a progressive increase in expenditure due to the aging of the population. In fact, hip and knee arthroplasty surgery are mainly due to primary osteoarthritis that affects the elderly population. This study was carried out with the aim of analysing the introduction of the fast track surgery protocol, through the lean Six Sigma, on patients undergoing knee and hip prosthetic replacement surgery. The goal was to improve the arthroplasty surgery process by reducing the average length of stay (LOA) and hospital costsDesign/methodology/approachLean Six Sigma was applied to evaluate the arthroplasty surgery process through the DMAIC cycle (define, measure, analyse, improve and control) and the lean tools (value stream map), adopted to analyse the new protocol and improve process performance. The dataset consisted of two samples of patients: 54 patients before the introduction of the protocol and 111 patients after the improvement. Clinical and demographic variables were collected for each patient (gender, age, allergies, diabetes, cardiovascular diseases and American Society of Anaesthesiologists (ASA) score).FindingsThe results showed a 12.70% statistically significant decrease in LOS from an overall average of 8.72 to 7.61 days. Women patients without allergies, with a low ASA score not suffering from diabetes and cardiovascular disease showed a significant a reduction in hospital days with the implementation of the FTS protocol. Only the age variable was not statistically significant.Originality/valueThe introduction of the FTS in the orthopaedic field, analysed through the LSS, demonstrated to reduce LOS and, consequently, costs. For each individual patient, there was an economic saving of € 445.85. Since our study takes into consideration a dataset of 111 patients post-FTS, the overall economic saving brought by this study amounts to €49,489.35.

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“…It is a methodology of data analysis, a branch of artificial intelligence, that enables systems to learn and improve from data [ 2 ]. The ML methodology is spreading in clinical research with applications in several medical fields, such as neurology, cardiology, ophthalmology, pediatrics, and fetal monitoring [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

et al. 2021

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Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.

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Classifying Different Stages of Parkinson’s Disease Through Random Forests

Cited by 27 publications

References 25 publications

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

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

Implementing fast track surgery in hip and knee arthroplasty using the lean Six Sigma methodology

Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure

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