Toward predicting medical conditions using k-nearest neighbors

Tayeb, Shahab; Pirouz, Matin; Sun, Johann; Hall, Kaylee; Chang, Andrew S.; Li, Jessica; Song, Connor; Chauhan, Apoorva; Ferra, Michael; Sager, Theresa; Zhan, Justin; Latifi, Shahram

doi:10.1109/bigdata.2017.8258395

Cited by 24 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two machine learning algorithms, which were the k-nearest neighbor (KNN) and the artificial neural network (ANN), were used for developing prediction models. The KNN algorithm is one of the most extensively used data mining tool to classify and predict patterns of health informatics data [36,37]. The KNN algorithm predicts that similar objects would exist in close proximity; as a result, it labels the class of the target based on its surrounding k neighbors [38].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…The numbers of k examined ranged from 1 to 10 and the hidden neurons examined were 2, 3, 4, 5 and 6. These values were selected based on suggestions from the KNN and ANN literatures [7,[36][37][38][39][40][41][42]. We found that k = 3 (KNN model) and the hidden neurons = 4 in one hidden layer (ANN model) provided the best prediction accuracy.…”

Section: Feature Selection Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Thakkar

Liao

et al. 2020

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models. Methods This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models. Results Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77. Conclusions Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

show abstract

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: Feature Selection Proceduresmentioning

confidence: 99%

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Thakkar

Liao

et al. 2020

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…K-NN has been extensively used in the medical eld with a relatively high rate of success compared to other methods like Linear Discriminant Analysis (LDA). 43,44 The basic underlying hypothesis of K-NN is that if two datapoints have a high degree of similarity, there is a high probability that they belong to the same class. In other words, the probability of two data points belonging to the same class is proportional to their degree of proximity or similarity.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Subasi et al [11] used different algorithms for the training of phishing detection models. These algorithms included Artificial Neural Networks (ANN) [1], [12], [13], K-Nearest Neighbor (KNN) [14], Support Vector Machine (SVM) [15], [16], C4.5 Decision Tree [17], [18], Random Forest (RF) [19], etc. According to the analysis results of this experiment, the authors proposed that the dataset provided by the UCI machine learning repository [20] was more suitable for training and prediction through a treestructured algorithm.…”

Section: Heuristics Analysismentioning

confidence: 99%

“…In this study, we collected 24471 phishing sites from PhishTank in the collection model, with 3850 legitimate sites retrieved from the target column of the corresponding is ip address f 16 script block rate f 3 dots f 17 style block rate f 4 is special words f 18 get title feature f 5 url linkin num f 19 is login form f 6 url traffic rank f 20 is with whois f 7 get kbytes f 21 get time f 8 is frame f 22 is redirect f 9 is meta redirect f 23 ipv4 numbers f 10 is meta base64 redirect f 24 ipv6 numbers f 11 same extern domain script rate f 25 organization f 12 same external domain link rate f 26 is alias f 13 same external domain img rate f 27 is weird serial f 14 external a tag same domain f 28 get day age phishing sites. Basically, the number of phishing sites was considerably larger than the number of legitimate sites, because hackers usually imitate a specific legitimate site and design multiple similar phishing sites.…”

Section: Training Datasetmentioning

confidence: 99%

AI@ntiPhish — Machine Learning Mechanisms for Cyber-Phishing Attack

Chen

2019

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X 2 , and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector Machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study. key words: anti-phishing, machine learning algorithm, ensemble learning mechanism, cyber attack

show abstract

Toward predicting medical conditions using k-nearest neighbors

Cited by 24 publications

References 12 publications

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips

AI@ntiPhish — Machine Learning Mechanisms for Cyber-Phishing Attack

Contact Info

Product

Resources

About