| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Oumer Abdulkadir Ebrahim, Getachew Derbe. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021. Scientific reports. vol 13. issue 1. 2023-05-13. PMID:37179444. |
from the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned j 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. |
2023-05-13 |
2023-08-14 |
Not clear |
| Azadeh Alizargar, Yang-Lang Chang, Tan-Hsu Ta. Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques. Bioengineering (Basel, Switzerland). vol 10. issue 4. 2023-04-28. PMID:37106668. |
the performances of these techniques were compared in terms of confusion matrix, precision, recall, f1 score, accuracy, receiver operating characteristics (roc), and the area under the curve (auc) to identify a method that is appropriate for predicting this disease. |
2023-04-28 |
2023-08-14 |
Not clear |
| Armita Salahi, Carlos Honrado, John Moore, Sara Adair, Todd W Bauer, Nathan S Swam. Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment. Biosensors & bioelectronics. vol 231. 2023-04-14. PMID:37058962. |
this is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and cafs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. |
2023-04-14 |
2023-08-14 |
Not clear |
| Damilola D Olatinwo, Adnan Abu-Mahfouz, Gerhard Hancke, Hermanus Myburg. IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients. Sensors (Basel, Switzerland). vol 23. issue 6. 2023-03-30. PMID:36991659. |
the proposed models are compared with a related existing model for evaluation and validation using standard performance metrics such as prediction accuracy, precision, recall, f1 score, confusion matrix, and the differences between the actual and predicted values. |
2023-03-30 |
2023-08-14 |
Not clear |
| Atsuyuki Inui, Hanako Nishimoto, Yutaka Mifune, Tomoya Yoshikawa, Issei Shinohara, Takahiro Furukawa, Tatsuo Kato, Shuya Tanaka, Masaya Kusunose, Ryosuke Kurod. Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach. Bioengineering (Basel, Switzerland). vol 10. issue 3. 2023-03-29. PMID:36978668. |
the model performance was compared using a confusion matrix. |
2023-03-29 |
2023-08-14 |
Not clear |
| Ummara Ayman, Muhammad Sultan Zia, Ofonime Dominic Okon, Najam-Ur Rehman, Talha Meraj, Adham E Ragab, Hafiz Tayyab Rau. Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method. Biomedicines. vol 11. issue 3. 2023-03-29. PMID:36979795. |
the proposed model's performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, f1-score, specificity, sensitivity, and the roc curve. |
2023-03-29 |
2023-08-14 |
human |
| Rashid M Ansari, Mark F Harris, Hassan Hosseinzadeh, Nicholas Zwa. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare (Basel, Switzerland). vol 11. issue 6. 2023-03-29. PMID:36981560. |
the diabetes dataset was split 80:20 between training and testing; 80% (160) instances were used for training purposes and 20% (40) instances were used for testing purposes, while the algorithms' overall performance was measured using a confusion matrix. |
2023-03-29 |
2023-08-14 |
human |
| Rashid M Ansari, Mark F Harris, Hassan Hosseinzadeh, Nicholas Zwa. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare (Basel, Switzerland). vol 11. issue 6. 2023-03-29. PMID:36981560. |
the logistic regression model performance was evaluated based on the confusion matrix. |
2023-03-29 |
2023-08-14 |
human |
| Rashid M Ansari, Mark F Harris, Hassan Hosseinzadeh, Nicholas Zwa. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare (Basel, Switzerland). vol 11. issue 6. 2023-03-29. PMID:36981560. |
the output of the confusion matrix showed that only 11 out of 200 patients were correctly assessed/classified as meeting diabetes self-management targets based on the values of hba1c < 7%. |
2023-03-29 |
2023-08-14 |
human |
| Won-Se Park, Jong-Ki Huh, Jae-Hong Le. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset. Scientific reports. vol 13. issue 1. 2023-03-25. PMID:36964171. |
the accuracy, precision, recall, f1 score, and confusion matrix were calculated to evaluate the classification performance of the automated dl algorithm. |
2023-03-25 |
2023-08-14 |
Not clear |
| Yaser A Nanehkaran, Zhu Licai, Mohammad Azarafza, Sona Talaei, Xu Jinxia, Junde Chen, Reza Derakhshan. The predictive model for COVID-19 pandemic plastic pollution by using deep learning method. Scientific reports. vol 13. issue 1. 2023-03-14. PMID:36914765. |
performance of the dnn-based model is controlled by the confusion matrix, receiver operating characteristic (roc) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, gaussian naïve bayes, logistic regression, and multilayer perceptron methods. |
2023-03-14 |
2023-08-14 |
Not clear |
| Shayan Shafiee, Jaidip Jagtap, Mykhaylo Zayats, Jonathan Epperlein, Anjishnu Banerjee, Aron Geurts, Michael Flister, Sergiy Zhuk, Amit Josh. Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer. Cancers. vol 15. issue 5. 2023-03-11. PMID:36900252. |
machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. |
2023-03-11 |
2023-08-14 |
Not clear |
| Dimas Chaerul Ekty Saputra, Khamron Sunat, Tri Ratnaningsi. A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia. Healthcare (Basel, Switzerland). vol 11. issue 5. 2023-03-11. PMID:36900702. |
this was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an f1 score of 98.84%. |
2023-03-11 |
2023-08-14 |
Not clear |
| Yonghun Kwon, Woojae Kim, Inbum Jun. Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing. Sensors (Basel, Switzerland). vol 23. issue 5. 2023-03-11. PMID:36904779. |
finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. |
2023-03-11 |
2023-08-14 |
Not clear |
| Mihaela Antonina Calin, Radu Robert Piticescu, Sorin Viorel Parasc. Comparative analysis of denoising techniques in burn depth discrimination from burn hyperspectral images. Journal of biophotonics. 2023-03-11. PMID:36906680. |
spectral angle mapper classifier was used for data classification and the confusion matrix was used for quantitative evaluation of the performances of the denoising methods. |
2023-03-11 |
2023-08-14 |
Not clear |
| Yang Zhou, Jinhua Feng, Shuya Mei, Han Zhong, Ri Tang, Shunpeng Xing, Yuan Gao, Qiaoyi Xu, Zhengyu H. MACHINE LEARNING MODELS FOR PREDICTING ACUTE KIDNEY INJURY IN PATIENTS WITH SEPSIS-ASSOCIATED ACUTE RESPIRATORY DISTRESS SYNDROME. Shock (Augusta, Ga.). vol 59. issue 3. 2023-03-07. PMID:36625493. |
the performance of all predictive models was evaluated using the area under receiver operating characteristic curve, precision-recall curve, confusion matrix, and calibration plot. |
2023-03-07 |
2023-09-07 |
Not clear |
| Orlando Iparraguirre-Villanueva, Aldo Alvarez-Risco, Jose Luis Herrera Salazar, Saul Beltozar-Clemente, Joselyn Zapata-Paulini, Jaime A Yáñez, Michael Cabanillas-Carbonel. The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model. Vaccines. vol 11. issue 2. 2023-02-28. PMID:36851190. |
the results also showed the polarity of feelings through the cnn-lstm confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. |
2023-02-28 |
2023-08-14 |
Not clear |
| J Jeba Sonia, Prassanna Jayachandran, Abdul Quadir Md, Senthilkumar Mohan, Arun Kumar Sivaraman, Kong Fah Te. Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm. Diagnostics (Basel, Switzerland). vol 13. issue 4. 2023-02-25. PMID:36832207. |
to provide experimental analysis and performances of diabetes diagnoses in terms of sensitivity, specificity, and accuracy, a confusion matrix is developed. |
2023-02-25 |
2023-08-14 |
Not clear |
| Simone De Fabritiis, Silvia Valentinuzzi, Gianluca Piras, Ilaria Cicalini, Damiana Pieragostino, Sara Pagotto, Silvia Perconti, Mirco Zucchelli, Alberto Schena, Elisa Taschin, Gloria Simona Berteşteanu, Diana Liberata Esposito, Antonio Stigliano, Vincenzo De Laurenzi, Francesca Schiavi, Mario Sanna, Piero Del Boccio, Fabio Verginelli, Renato Mariani-Costantin. Targeted metabolomics detects a putatively diagnostic signature in plasma and dried blood spots from head and neck paraganglioma patients. Oncogenesis. vol 12. issue 1. 2023-02-25. PMID:36841802. |
the best confusion matrix from the roc curve built on 2 metabolites, dado and c26:0-lpc, provided specificity of 94.29% and sensitivity of 89.29%, with positive and negative predictive values of 96.2% and 84.6%, respectively. |
2023-02-25 |
2023-08-14 |
Not clear |
| Shijun Xu, Wenbo Wu, Chuanxing Gong, Jinjian Dong, Caifei Qia. Identification of the interference spectra of edible oil samples based on neighborhood rough set attribute reduction. Applied optics. vol 62. issue 6. 2023-02-23. PMID:36821315. |
finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. |
2023-02-23 |
2023-08-14 |
Not clear |