Medical Health Big Data Classification Based on KNN Classification Algorithm

Xing, Wenchao; Bei, Yilin

doi:10.1109/access.2019.2955754

Cited by 169 publications

(50 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The gender and health professional features were the additional information required to conduct the COVID-19 test prioritization using the classification models. Gender was also used as a feature by prior works [ 4 , 20 , 36 , 37 ]. The use of exams such as CT images and blood tests limits classification models’ application scenarios because it is necessary to prioritize patients who are symptomatic for testing in the first days after symptoms onset.…”

Section: Discussionmentioning

confidence: 99%

“…A DT is an ML algorithm that usually uses a divide and conquer strategy to generate a directed acyclic graph by applying division rules based on information gain [ 20 ]. The algorithm has a built-in feature selection, and the information gain is guided by the concept of entropy H , which measures the randomness of a discrete random variable A (with domain a 1 , a 2 ,..., a n ), given by:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Santana¹,

Silveira²,

Sobrinho³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Controlling the COVID-19 outbreak in Brazil is a challenge due to the population’s size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. Objective The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. Methods Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models’ performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. Results Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. Conclusions The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Santana¹,

Silveira²,

Sobrinho³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…In order to classify the X-ray images, we selected widelyused machine learning methods in the literature: Bayes [43]- [45], random forest (RF) [46]- [48], multilayer perceptron (MLP) [49]- [51], k-nearest neighbors (kNN) [52]- [54], and SVM [55]- [57]. In the SVM classifier, we consider the linear and RBF kernels.…”

Section: Classification Stepsmentioning

confidence: 99%

Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

Ohata

Bezerra

Chagas

et al. 2021

IEEE/CAA J. Autom. Sinica

201

View full text Add to dashboard Cite

The new coronavirus (COVID-19), declared by the World Health Organization as a pandemic, has infected more than 1 million people and killed more than 50 thousand. An infection caused by COVID-19 can develop into pneumonia, which can be detected by a chest X-ray exam and should be treated appropriately. In this work, we propose an automatic detection method for COVID-19 infection based on chest X-ray images. The datasets constructed for this study are composed of 194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients. Since few images of patients with COVID-19 are publicly available, we apply the concept of transfer learning for this task. We use different architectures of convolutional neural networks (CNNs) trained on ImageNet, and adapt them to behave as feature extractors for the X-ray images. Then, the CNNs are combined with consolidated machine learning methods, such as k-Nearest Neighbor, Bayes, Random Forest, multilayer perceptron (MLP), and support vector machine (SVM). The results show that, for one of the datasets, the extractor-classifier pair with the best performance is the MobileNet architecture with the SVM classifier using a linear kernel, which achieves an accuracy and an F1-Score of 98.5%. For the other dataset, the best pair is DenseNet201 with MLP, achieving an accuracy and an F1-Score of 95.6%. Thus, the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.

show abstract

“…The gender and health professional features were the additional information required to conduct the COVID-19 test prioritization using the classification models. Gender was also used as a feature by prior works [4,20,36,37]. The use of exams such as CT images and blood tests limits classification models' application scenarios because it is necessary to prioritize patients who are symptomatic for testing in the first days after symptoms onset.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach (Preprint)

Santana¹,

Silveira²,

Sobrinho³

et al. 2021

Preprint

View full text Add to dashboard Cite

BACKGROUND controlling the COVID-19 outbreak in Brazil is considered a challenge of continental proportions due to the high population and urban density, weak implementation and maintenance of social distancing strategies, and limited testing capabilities. OBJECTIVE to contribute to addressing such a challenge, we present the implementation and evaluation of supervised Machine Learning (ML) models to assist the COVID-19 detection in Brazil based on early-stage symptoms. METHODS firstly, we conducted data preprocessing and applied the Chi-squared test in a Brazilian dataset, mainly composed of early-stage symptoms, to perform statistical analyses. Afterward, we implemented ML models using the Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. We evaluated the ML models using precision, accuracy score, recall, the area under the curve, and the Friedman and Nemenyi tests. Based on the comparison, we grouped the top five ML models and measured feature importance. RESULTS the MLP model presented the highest mean accuracy score, with more than 97.85%, when compared to GBM (> 97.39%), RF (> 97.36%), DT (> 97.07%), XGBoost (> 97.06%), KNN (> 95.14%), and SVM (> 94.27%). Based on the statistical comparison, we grouped MLP, GBM, DT, RF, and XGBoost, as the top five ML models, because the evaluation results are statistically indistinguishable. The ML models` importance of features used during predictions varies from gender, profession, fever, sore throat, dyspnea, olfactory disorder, cough, runny nose, taste disorder, and headache. CONCLUSIONS supervised ML models effectively assist the decision making in medical diagnosis and public administration (e.g., testing strategies), based on early-stage symptoms that do not require advanced and expensive exams.

show abstract

Medical Health Big Data Classification Based on KNN Classification Algorithm

Cited by 169 publications

References 23 publications

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach (Preprint)

Contact Info

Product

Resources

About