Accuracy, sensitivity and specificity of our algorithm (DBC), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (ADA) and Mean based method (MU) at prediction window of 120 minutes, averaged over 144 choices of [O,T] settings and over (LEFT:) 3683 patients (online)/ (RIGHT:) 5–fold cross validation (offline).

Comparing Predictive Performance of Statistical Learning Models on Medical Data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the “large N, small p” setting

Austin, Peter C.
Harrell, Frank E.
Steyerberg, Ewout W.

April 2021

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical ou...

Prediction performance comparison based on the maximum, average, and minimum accuracy of the six models compared with the accuracy of the training set.

Kraisak Kesorn (3277905)
Phatsavee Ongruk (737311)
Jakkrawarn Chompoosri (737312)
Atchara Phumee (530776)
Usavadee Thavara (673117)
Apiwat Tawatsin (737313)
Padet Siriyasatien (669580)

May 2015

The experiment was conducted 10 times using 10-fold cross-validation performed on the training se...

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, RF, SVM, ADA and MU—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.

Sakyajit Bhattacharya (4885030)
Vijay Huddar (4885033)
Vaibhav Rajan (4885027)
Chandan K. Reddy (749750)

February 2018

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, RF, SVM, AD...

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, Linear SVM—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.

Sakyajit Bhattacharya (4885030)
Vijay Huddar (4885033)
Vaibhav Rajan (4885027)
Chandan K. Reddy (749750)

February 2018

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, Linear SVM—...

Classification accuracy for each of the machine learning approaches: Naive Bayesian, Linear Discriminant analysis (LDA) and Support Vector Machines (SVM).

M. C. van der Steen (542668)
Floris T. van Vugt (519310)
Peter E. Keller (463085)
Eckart Altenmüller (21294)

March 2014

For each approach, the classification rate for the model that was trained on all data (all) and t...

Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

Prasetyo, Aditya Budi
Laksana, Tri Ginanjar

December 2022

The problem that occurs in the application of K-Nearest Neighbors as a classification algorithm is t...

Effect of data scaling methods on machine learning algorithms and model performance

Ahsan, M M
Mahmud, M A Parvez
Saha, P K
Gupta, K D
Siddique, Z

January 2021

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a s...

Towards an accurate cancer diagnosis modelization: Comparison of random forest strategies

Debit, Ahmed

October 2019

Machine learning approaches are heavily used to produce models that will one day support clinical d...

The accuracy of MLP and k-NN classifiers with the selected features subsets (individual and combinational) for Healthy and patients prone to SCD.

Elias Ebrahimzadeh (519550)
Mohammad Pooyan (331258)
Ahmad Bijar (422520)

February 2014

The accuracy of MLP and k-NN classifiers with the selected features subsets (individual and combi...

TOWARDS AN ACCURATE CANCER DIAGNOSIS MODELIZATION:COMPARISON OF RANDOM FOREST STRATEGIES

Debit, Ahmed
Poulet, Christophe
JOSSE, Claire
Azencott, Chloe-Agathe
Jerusalem, Guy
Van Steen, Kristel
BOURS, Vincent

March 2019

Machine learning approaches are heavily used to produce models that will one day support...

Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminant Analysis (LDA) models built to classify patients with prostate cancer and cancer-free controls based on blood PSA levels and urinary VOCs.

Tanzeela Khalid (431507)
Raphael Aggio (830167)
Paul White (431508)
Ben De Lacy Costello (5655478)
Raj Persad (431509)
Huda Al-Kateb (683878)
Peter Jones (199862)
Chris S. Probert (389092)
Norman Ratcliffe (171878)

November 2015

Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminan...

Comparing predictive performance of statistical learning models on Medical data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Prediction accuracy of the supervised learning algorithms for the datasets of different sampling resolutions.

Jinxiang Xi (518144)
Weizhong Zhao (801902)

January 2019

(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...

Classification accuracy of Patient F.

Ujwal Chaudhary (3708253)
Bin Xia (162982)
Stefano Silvoni (288897)
Leonardo G. Cohen (356180)
Niels Birbaumer (36361)

January 2017

Linear SVM CA across “training sessions—offline CA” (histogram in grey), “feedback sessions—onlin...

Comparing Predictive Performance of Statistical Learning Models on Medical Data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the “large N, small p” setting

Austin, Peter C.
Harrell, Frank E.
Steyerberg, Ewout W.

April 2021

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical ou...

Prediction performance comparison based on the maximum, average, and minimum accuracy of the six models compared with the accuracy of the training set.

Kraisak Kesorn (3277905)
Phatsavee Ongruk (737311)
Jakkrawarn Chompoosri (737312)
Atchara Phumee (530776)
Usavadee Thavara (673117)
Apiwat Tawatsin (737313)
Padet Siriyasatien (669580)

May 2015

The experiment was conducted 10 times using 10-fold cross-validation performed on the training se...

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, RF, SVM, ADA and MU—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.

Sakyajit Bhattacharya (4885030)
Vijay Huddar (4885033)
Vaibhav Rajan (4885027)
Chandan K. Reddy (749750)

February 2018

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, RF, SVM, AD...

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, Linear SVM—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.

Sakyajit Bhattacharya (4885030)
Vijay Huddar (4885033)
Vaibhav Rajan (4885027)
Chandan K. Reddy (749750)

February 2018

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, Linear SVM—...

Classification accuracy for each of the machine learning approaches: Naive Bayesian, Linear Discriminant analysis (LDA) and Support Vector Machines (SVM).

M. C. van der Steen (542668)
Floris T. van Vugt (519310)
Peter E. Keller (463085)
Eckart Altenmüller (21294)

March 2014

For each approach, the classification rate for the model that was trained on all data (all) and t...

Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit

Prasetyo, Aditya Budi
Laksana, Tri Ginanjar

December 2022

The problem that occurs in the application of K-Nearest Neighbors as a classification algorithm is t...

Effect of data scaling methods on machine learning algorithms and model performance

Ahsan, M M
Mahmud, M A Parvez
Saha, P K
Gupta, K D
Siddique, Z

January 2021

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a s...

Towards an accurate cancer diagnosis modelization: Comparison of random forest strategies

Debit, Ahmed

October 2019

Machine learning approaches are heavily used to produce models that will one day support clinical d...

The accuracy of MLP and k-NN classifiers with the selected features subsets (individual and combinational) for Healthy and patients prone to SCD.

Elias Ebrahimzadeh (519550)
Mohammad Pooyan (331258)
Ahmad Bijar (422520)

February 2014

The accuracy of MLP and k-NN classifiers with the selected features subsets (individual and combi...

TOWARDS AN ACCURATE CANCER DIAGNOSIS MODELIZATION:COMPARISON OF RANDOM FOREST STRATEGIES

Debit, Ahmed
Poulet, Christophe
JOSSE, Claire
Azencott, Chloe-Agathe
Jerusalem, Guy
Van Steen, Kristel
BOURS, Vincent

March 2019

Machine learning approaches are heavily used to produce models that will one day support...

Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminant Analysis (LDA) models built to classify patients with prostate cancer and cancer-free controls based on blood PSA levels and urinary VOCs.

Tanzeela Khalid (431507)
Raphael Aggio (830167)
Paul White (431508)
Ben De Lacy Costello (5655478)
Raj Persad (431509)
Huda Al-Kateb (683878)
Peter Jones (199862)
Chris S. Probert (389092)
Norman Ratcliffe (171878)

November 2015

Accuracy results of repeated 10 fold cross validation of the Random Forest and Linear Discriminan...

Comparing predictive performance of statistical learning models on Medical data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Prediction accuracy of the supervised learning algorithms for the datasets of different sampling resolutions.

Jinxiang Xi (518144)
Weizhong Zhao (801902)

January 2019

(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...

Classification accuracy of Patient F.

Ujwal Chaudhary (3708253)
Bin Xia (162982)
Stefano Silvoni (288897)
Leonardo G. Cohen (356180)
Niels Birbaumer (36361)

January 2017

Linear SVM CA across “training sessions—offline CA” (histogram in grey), “feedback sessions—onlin...

Comparing Predictive Performance of Statistical Learning Models on Medical Data

Biney, Francis

January 2020

This work investigates the predictive performance of 10 Machine learning models on three medical dat...

Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the “large N, small p” setting

Austin, Peter C.
Harrell, Frank E.
Steyerberg, Ewout W.

April 2021

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical ou...

Prediction performance comparison based on the maximum, average, and minimum accuracy of the six models compared with the accuracy of the training set.

Kraisak Kesorn (3277905)
Phatsavee Ongruk (737311)
Jakkrawarn Chompoosri (737312)
Atchara Phumee (530776)
Usavadee Thavara (673117)
Apiwat Tawatsin (737313)
Padet Siriyasatien (669580)

May 2015

The experiment was conducted 10 times using 10-fold cross-validation performed on the training se...

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