Predicting Kidney Failure, Cardiovascular Disease and Death in Advanced CKD Patients

Ramspek, Chava; Boekee, Rosemarijn; Evans, Marie; Heimbürger, Olof; Snead, Charlotte M.; Caskey, Fergus; Torino, Claudia; Porto, Gaetana; Szymczak, Maciej; Krajewska, Magdalena; Drechsler, Christiane; Wanner, Christoph; Chesnaye, Nicholas; Jager, Kitty J.; Dekker, Friedo W.; Snoeijs, Maarten G.; Rotmans, Joris I.; Diepen, Merel van

doi:10.1016/j.ekir.2022.07.165

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…Early detection and treatment using ML techniques have the potential to significantly improve patient survival rates. Consequently, several studies have utilized ML for the diagnosis [15]- [19] and prediction of HF, such as determining the likelihood of a patient having a disease history that may cause HF, such as hypertension, diabetes, or hyperlipidemia [20]- [23]. Various classification algorithms, including decision trees [24]- [26], support vector machines (SVM) [27], Naïve Bayes [28], and neural networks [29] have been used for HF prediction.…”

Section: Introductionmentioning

confidence: 99%

Integration of PSO-based advanced supervised learning techniques for classification data mining to predict heart failure

Mesran,

Kmurawak,

Windarto

2023

TELKOMNIKA

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Heart failure (HF) is a global health threat, requiring urgent research in its classification. This study proposes a novel approach for HF classification by integrating advanced supervised learning (ASL) and particle swarm optimization (PSO). ASL techniques like bagging and AdaBoost are employed within the PSO+ASL optimization model to enhance prediction accuracy. PSO optimizes model weights and bias, while ASL addresses overfitting or underfitting issues. Split validation and cross-validation (70:30, 80:20, 90:10 with k-fold=10) are used for further optimization. The testing phase involves 12 classifiers in five groups: decision tree models (DTM), support vector machines (SVM), Naïve Bayes classifiers models (NBCM), logistic regression models (LRM), and lazy model (LM). Evaluating the proposed approach with an HF patient dataset from https://www.kaggle.com, results are compared against the standard model, PSO optimization, and PSO+ASL. Experimental findings demonstrate the superiority of the proposed approach, achieving higher accuracy in HF prediction. The PSO+ASL optimization model with the k-nearest neighbor (k-NN) method exhibits the best classification performance. It consistently achieves the highest accuracy across all tests on dataset composition ratios, with 100% accuracy, f-measure, sensitivity, specificity values, and area under cover (AUC) of 1. The proposed approach serves as a reliable tool for early detection and prevention of HF.

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Section: Introductionmentioning

confidence: 99%

Integration of PSO-based advanced supervised learning techniques for classification data mining to predict heart failure

Mesran,

Kmurawak,

Windarto

2023

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…In this issue of Kidney International Reports, Ramspeck et al. 16 make a valuable contribution by externally validating the Grams RPE among 1517 participating patients in the European Quality Study. Starting in 2012, The European Quality Study recruited patients 65 years or older from nephrology clinics in 6 European countries with eGFR values at baseline between 10 and 30 ml/min.…”

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confidence: 99%

“…In the analysis by Ramspeck et al. , 16 the investigators sensibly use cumulative incidence functions to estimate observed 2-year and 4-year observed risks but it should be pointed out that these are not the same as multinomial probabilities. This may account for some of the difference between predicted and observed risks.…”

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confidence: 99%

“…The investigators employ decision curve analysis to consider the clinical effect of decision-making with respect to initiation of KFRT preparation using the Grams and KFRE RPEs and a simpler rule based solely on eGFR. 16 Decision curve analysis is a validated approach but some readers may be unfamiliar with it. The basic idea is to consider positive tests (i.e., predicted KFRT risks above a given threshold).…”

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confidence: 99%

“…Ramspeck et al. 16 have taken on the difficult but important task of externally validating a potentially more useful RPE for patients with stage G4 and G5 CKD. Decision-making, with respect to access to MDT care and initiation of planning for KFRT, are fraught in this population making the insights from RPEs potentially useful.…”

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confidence: 99%

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