scholarly journals Comparing the performance of risk stratification scores in Brugada syndrome: a multi-centre study

2021 ◽  
Author(s):  
Sharen Lee ◽  
Jiandong Zhou ◽  
George Bazoukis ◽  
Konstantinos P Letsas ◽  
Tong Liu ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Brugada Syndrome ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Risk Scores ◽  
Predictive Score ◽  
Intermediate Risk ◽  
Ventricular Tachyarrhythmias

Introduction: The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. The present study evaluated the predictive performance of different risk scores in an Asian BrS population and its intermediate risk subgroup. Methods: This is a retrospective territory-wide cohort study of consecutive patients diagnosed with BrS from January 1st, 1997 to June 20th, 2020 in Hong Kong. The primary outcome is sustained ventricular tachyarrhythmias. A novel predictive score was developed. Machine learning-based nearest neighbor and Gaussian Naive Bayes models were also developed. The area under the receiver operator characteristic (ROC) curve (AUC) was compared between the different scores. Results: The cohort consists of 548 consecutive BrS patients (7% female, age at diagnosis: 50+/-16 years old, follow-up duration: 84+/-55 months). For risk stratification in the whole BrS cohort, the score developed by Sieira et al. showed the best performance with an AUC of 0.805, followed by the Shanghai score (0.698), and the scores by Okamura et al. (0.667), Delise et al. (0.661), Letsas et al. (0.656) and Honarbakhsh et al. (0.592). A novel risk score was developed based on variables and weighting from the best performing score (the Sieira score), with the inclusion of additional variables significant on univariable Cox regression (arrhythmias other than ventricular tachyarrhythmias, early repolarization pattern in the peripheral leads, aVR sign, S-wave in lead I and QTc ≥436 ms). This score has the highest AUC of 0.855 (95% CI: 0.808-0.901). The Gaussian Naive Bayes model demonstrated the best performance (AUC: 0.97) compared to logistic regression and nearest neighbor models. Conclusion: The inclusion of investigation results and more complex models are needed to improve the predictive performance of risk scores in the intermediate risk BrS population.

Robustness and relevance of predictive score in sudden cardiac death for patients with Brugada syndrome

2020 ◽  
Author(s):  
Vincent Probst ◽  
Thomas Goronflot ◽  
Soraya Anys ◽  
Romain Tixier ◽  
Jean Briand ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Brugada Syndrome ◽  
Sudden Cardiac Arrest ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Predictive Score ◽  
Intermediate Risk ◽  
Predictive Capacity ◽  
Main Challenge ◽  
Arrhythmic Event

Abstract Aims  Risk stratification of sudden cardiac arrest (SCA) in Brugada syndrome (Brs) remains the main challenge for physicians. Several scores have been suggested to improve risk stratification but never replicated. We aim to investigate the accuracy of the Brs risk scores. Methods and results  A total of 1613 patients [mean age 45 ± 15 years, 69% male, 323 (20%) symptomatic] were prospectively enrolled from 1993 to 2016 in a multicentric database. All data described in the risk score were double reviewed for the study. Among them, all patients were evaluated with Shanghai score and 461 (29%) with Sieira score. After a mean follow-up of 6.5 ± 4.7 years, an arrhythmic event occurred in 75 (5%) patients including 16 SCA, 11 symptomatic ventricular arrhythmia, and 48 appropriate therapies. Predictive capacity of the Shanghai score (n = 1613) and the Sieira (n = 461) score was, respectively, estimated by an area under the curve of 0.73 (0.67–0.79) and 0.71 (0.61–0.81). Considering Sieira score, the event rate at 10 years was significantly higher with a score of 5 (26.4%) than with a score of 0 (0.9%) or 1 (1.1%) (P < 0.01). No statistical difference was found in intermediate-risk patients (score 2–4). The Shanghai score does not allow to better stratify the risk of SCA. Conclusions  In the largest cohort of Brs patient ever described, risk scores do not allow stratifying the risk of arrhythmic event in intermediate-risk patient.

scholarly journals Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 2193-2197
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Specific Pattern ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Digital Format ◽  
Tree Classifier

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

scholarly journals RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

2019 ◽  
Vol 9 (6) ◽  
pp. 4866
Author(s):  
Rajni Rajni ◽  
Amandeep Amandeep
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Early Stage ◽  
Human Life ◽  
Naïve Bayes ◽  
Support Vector ◽  
Pima Indians ◽  
K Nearest Neighbor ◽  
Fast Pace ◽  
Bayes Algorithm

<p>Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.</p>

Towards Optimal Naive Bayes Nearest Neighbor

2010 ◽  
pp. 171-184 ◽  
Author(s):  
Régis Behmo ◽  
Paul Marcombes ◽  
Arnak Dalalyan ◽  
Véronique Prinet
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes

scholarly journals Prediksi Harga Minyak Kelapa Sawit Dalam Investasi Dengan Membandingkan Algoritma Naïve Bayes, Support Vector Machine dan K-Nearest Neighbor

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Deny Haryadi ◽  
Rila Mandala
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor

Harga minyak kelapa sawit bisa mengalami kenaikan, penurunan maupun tetap setiap hari karena faktor yang mempengaruhi harga minyak kelapa sawit seperti harga minyak nabati lain (minyak kedelai dan minyak canola), harga minyak mentah dunia, maupun nilai tukar riil antara kurs dolar terhadap mata uang negara produsen (rupiah, ringgit, dan canada) atau mata uang negara konsumen (rupee). Untuk itu dibutuhkan prediksi harga minyak kelapa sawit yang cukup akurat agar para investor bisa mendapatkan keuntungan sesuai perencanaan yang dibuat. tujuan dari penelitian ini yaitu untuk mengetahui perbandingan accuracy, precision, dan recall yang dihasilkan oleh algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam menyelesaikan masalah prediksi harga minyak kelapa sawit dalam investasi. Berdasarkan hasil pengujian dalam penelitian yang telah dilakukan, algoritma Support Vector Machine memiliki accuracy, precision, dan recall dengan jumlah paling tinggi dibandingkan dengan algoritma Naïve Bayes dan algoritma K-Nearest Neighbor. Nilai accuracy tertinggi pada penelitian ini yaitu 82,46% dengan precision tertinggi yaitu 86% dan recall tertinggi yaitu 89,06%.

Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers

2010 ◽  
Vol 5 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Mohammed J. Islam ◽  
Q. M. Jonathan Wu ◽  
Majid Ahmadi ◽  
Maher A. SidAhmed
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifiers

scholarly journals Machine Learning Readmission Risk Modeling: A Pediatric Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Patricio Wolff ◽  
Manuel Graña ◽  
Sebastián A. Ríos ◽  
Maria Begoña Yarza
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Naive Bayes ◽  
Class Imbalance ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Distribution Model ◽  
Training Dataset ◽  
Support Vector ◽  
Pediatric Hospital

Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

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