The analysis of the effects of acute rheumatic fever in childhood on cardiac disease with data mining

Emre, İlkim Ecem; Erol, Nurdan; Ayhan, Yusuf İzzet; Özkan, Yalçın; Erol, Çiğdem

doi:10.1016/j.ijmedinf.2018.12.009

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…Moreover, complex clinical fields such as family and community care—due to their intrinsic characteristics and burdens—could benefit from a system synthesising the tremendous amount of data from the NSOs in a structured dataset. Data-mining algorithms have the potential to discover meaningful pieces of information and tendencies in this vast net of data that could be transferred into direct knowledge in the clinical field [ 127 ].…”

Section: Discussionmentioning

confidence: 99%

The State of the Evidence about the Family and Community Nurse: A Systematic Review

Dellafiore

Caruso

Cossu³

et al. 2022

IJERPH

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Introduction. The increase in chronic degenerative diseases poses many challenges to the efficacy and sustainability of healthcare systems, establishing the family and community nurse (FCN) who delivers primary care as a strategic role. FCNs, indeed, can embrace the complexity of the current healthcare demand, sustain the ageing of the population, and focus on illness prevention and health promotion, ensuring a continuous and coordinated integration between hospitals and primary care ser. The literature on FCNs is rich but diverse. This study aimed to critically summarise the literature about the FCN, providing an overall view of the recent evidence. Methods. A state-of-art systematic review was performed on PubMed, CINAHL, and Scopus, employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and checklist to guide the search and reporting. Results. Five interpretative themes emerged from the 90 included articles: clinical practice, core competencies, outcomes, Organisational and educational models, and advanced training program. Conclusions. FCNs can make a major contribution to a population’s health, playing a key role in understanding and responding to patients’ needs. Even if the investment in prevention does not guarantee immediate required strategies and foresight on the part of decisionmakers, it is imperative to invest more political, institutional, and economic resources to support and ensure the FCNs’ competencies and their professional autonomy.

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Section: Discussionmentioning

confidence: 99%

The State of the Evidence about the Family and Community Nurse: A Systematic Review

Dellafiore

Caruso

Cossu³

et al. 2022

IJERPH

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“…C5.0 is a decision tree algorithm, and it is the improved version of the C4.5 algorithm. The boosting feature of C5.0 helps improve the accuracy of the model (Emre et al, 2019). The C50 is a large decision tree and gives the acknowledge of noise and missing data.…”

Section: Computational Operation and Methodsmentioning

confidence: 99%

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Carvalho¹,

Pereira²,

Filho³

et al. 2020

Revista Brasileira De Ciência Do Solo

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Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest -RF, Gradient Boosted Machine -GBM, C5.0, and multinomial log-linear model -MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by ROIC. To evaluate the random effect to split the dataset, we ran each model 50 times and observed the overall accuracy (OA) and kappa index, and uncertainty, majority and variety maps. The OA of Dataset01 to 04 was lower than to Dataset05 accuracy. However, map outputs of RF and GBM for Dataset01 and Dataset05 had the same majority prediction. It seems that RF and GBM produce consistent results in map outputs according to this methodology and pedologist expertise. To evaluate the uncertainty and the consistency of soil unit prediction, we used the majority maps process. Random Forest, similar to GBM, presented the best results. The increase in the number of covariates was not a guarantee of improvement in the OA or in the quality of the map output. Geographic position and distance raster did not improve the map output according to expert evaluation. Because the variance between the ROICs, when the training and validation datasets were split based on it, the subsets are quite different in relation to the covariates, and this is the reason for the worse results of this model, comparing with the Dataset05. On the other hand, when considering one complete dataset not based on ROICs, the variance of training and validation subsets is lower and produced more accurate parameters of quality.

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“…Its success can be attributed to its ability to identify patterns and relationships in large datasets, which can inform medical decisions and treatment plans. This technology has provided valuable insights into various medical conditions and has the potential to improve patient outcomes in the future 4–13 . Unfortunately, the prediction of the amputation risk of diabetic foot patients is not as straightforward when a vast array of machine‐learning classification algorithms is being utilized.…”

Section: Introductionmentioning

confidence: 99%

“…This technology has provided valuable insights into various medical conditions and has the potential to improve patient outcomes in the future. 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 Unfortunately, the prediction of the amputation risk of diabetic foot patients is not as straightforward when a vast array of machine‐learning classification algorithms is being utilized.…”

Section: Introductionmentioning

confidence: 99%

Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center

Demirkol,

Erol,

Tannier

et al. 2024

International Wound Journal

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Diabetic foot ulcers can have vital consequences, such as amputation for patients. The primary purpose of this study is to predict the amputation risk of diabetic foot patients using machine‐learning classification algorithms. In this research, 407 patients treated with the diagnosis of diabetic foot between January 2009–September 2019 in Istanbul University Faculty of Medicine in the Department of Undersea and Hyperbaric Medicine were retrospectively evaluated. Principal Component Analysis (PCA) was used to identify the key features associated with the amputation risk in diabetic foot patients within the dataset. Thus, various prediction/classification models were created to predict the “overall” risk of diabetic foot patients. Predictive machine‐learning models were created using various algorithms. Additionally to optimize the hyperparameters of the Random Forest Algorithm (RF), experimental use of Bayesian Optimization (BO) has been employed. The sub‐dimension data set comprising categorical and numerical values was subjected to a feature selection procedure. Among all the algorithms tested under the defined experimental conditions, the BO‐optimized “RF” based on the hybrid approach (PCA‐RF‐BO) and “Logistic Regression” algorithms demonstrated superior performance with 85% and 90% test accuracies, respectively. In conclusion, our findings would serve as an essential benchmark, offering valuable guidance in reducing such hazards.

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The analysis of the effects of acute rheumatic fever in childhood on cardiac disease with data mining

Cited by 21 publications

References 13 publications

The State of the Evidence about the Family and Community Nurse: A Systematic Review

The State of the Evidence about the Family and Community Nurse: A Systematic Review

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center

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