| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Rajib Kumar Halder, Mohammed Nasir Uddin, Md Ashraf Uddin, Sunil Aryal, Sajeeb Saha, Rakib Hossen, Sabbir Ahmed, Mohammad Abu Tareq Rony, Mosammat Farida Akte. ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application. Journal of pathology informatics. vol 15. 2024-03-21. PMID:38510072. |
the effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the area under the curve (auc) specifically for the classification of positive cases. |
2024-03-21 |
2024-03-23 |
Not clear |
| Abdirizak A Hassan, Abdisalam Hassan Muse, Christophe Chesnea. Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia. Scientific reports. vol 14. issue 1. 2024-03-13. PMID:38472298. |
evaluation metrics, such as confusion matrix, accuracy, precision, sensitivity, specificity, recall, f1 score, and area under the receiver operating characteristic (auroc), are employed to assess the performance of predictive models. |
2024-03-13 |
2024-03-15 |
Not clear |
| Shafiullah Khan, Muhammad Altaf Khan, Noha Alnazzaw. Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks. Sensors (Basel, Switzerland). vol 24. issue 5. 2024-03-13. PMID:38475178. |
the proposed system's performance was assessed using a confusion matrix. |
2024-03-13 |
2024-03-15 |
Not clear |
| Anitha T, Gopu G, Arun Mozhi Devan P, Maher Assaa. Machine learning algorithm for ventilator mode selection, pressure and volume control. PloS one. vol 19. issue 3. 2024-03-13. PMID:38478485. |
different approaches, such as decision trees, optimizable bayes trees, naive bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and f1 score. |
2024-03-13 |
2024-03-16 |
Not clear |
| Abin Jose, Rijo Roy, Daniel Moreno-Andrés, Johannes Stegmaie. Automatic detection of cell-cycle stages using recurrent neural networks. PloS one. vol 19. issue 3. 2024-03-11. PMID:38466708. |
to study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well. |
2024-03-11 |
2024-03-14 |
Not clear |
| Kwaku Peprah Adjei, Anders Gravbrøt Finstad, Wouter Koch, Robert Brian O'Har. Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance. Ecology and evolution. vol 14. issue 3. 2024-03-08. PMID:38455149. |
the proposed model assumes a multinomial generalised linear model for the classification confusion matrix. |
2024-03-08 |
2024-03-10 |
Not clear |
| Yueru Xu, Wei Ye, Yuanchang Xie, Chen Wan. A two-dimensional surrogate safety measure based on fuzzy logic model. Accident; analysis and prevention. vol 199. 2024-03-05. PMID:38442630. |
fl-ittc is compared with other two-dimensional ssm including anticipated collision time (act) and probabilistic driving risk field (pdrf) based on the confusion matrix and the receiver operating characteristic (roc) curve. |
2024-03-05 |
2024-03-08 |
Not clear |
| Jun-Bo Tu, Wei-Jie Liao, Wen-Cai Liu, Xing-Hua Ga. Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data. Scientific reports. vol 14. issue 1. 2024-03-04. PMID:38438569. |
the confusion matrix, lift curve and calibration curves indicated that the stacker model had optimal clinical utility. |
2024-03-04 |
2024-03-07 |
Not clear |
| Abdullah A Asiri, Ahmad Shaf, Tariq Ali, Muhammad Ahmad Pasha, Aiza Khan, Muhammad Irfan, Saeed Alqahtani, Ahmad Alghamdi, Ali H Alghamdi, Abdullah Fahad A Alshamrani, Magbool Alelyani, Sultan Alamr. Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis. PeerJ. Computer science. vol 10. 2024-03-04. PMID:38435590. |
utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. |
2024-03-04 |
2024-03-06 |
Not clear |
| Endang Sri Rahayu, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama, Mauridhi Hery Purnom. A Combination Model of Shifting Joint Angle Changes with 3D-Deep Convolutional Neural Network to Recognize Human Activity. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol PP. 2024-02-29. PMID:38421841. |
model performance was evaluated using the confusion matrix. |
2024-02-29 |
2024-03-03 |
human |
| Heyu Zhang, Yuqiao Zheng, Jieshan L. A novel bearing current signal diagnosis method combining variational modal decomposition and improved random forests. The Review of scientific instruments. vol 95. issue 2. 2024-02-27. PMID:38411468. |
finally, the accuracy rate and confusion matrix are adopted to evaluate the prediction effects of both established and traditional models. |
2024-02-27 |
2024-03-02 |
Not clear |
| Shingo Taki, Takeshi Imura, Tsubasa Mitsutake, Yuji Iwamoto, Ryo Tanaka, Naoki Imada, Hayato Araki, Osamu Arak. Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study. Frontiers in neurorobotics. vol 18. 2024-02-23. PMID:38390525. |
we evaluated the validity of logistic regression analysis by using several indicators, such as the area under the curve (auc), and a confusion matrix. |
2024-02-23 |
2024-02-25 |
human |
| Shingo Taki, Takeshi Imura, Tsubasa Mitsutake, Yuji Iwamoto, Ryo Tanaka, Naoki Imada, Hayato Araki, Osamu Arak. Identifying the characteristics of patients with stroke who have difficulty benefiting from gait training with the hybrid assistive limb: a retrospective cohort study. Frontiers in neurorobotics. vol 18. 2024-02-23. PMID:38390525. |
furthermore, after building a confusion matrix, the calculated binary accuracy, sensitivity (recall), and specificity were 0.80, 0.80, and 0.81, respectively, indicated high accuracy. |
2024-02-23 |
2024-02-25 |
human |
| Zhengyang Lan, Mathieu Lempereur, Gwenael Gueret, Laetitia Houx, Marine Cacioppo, Christelle Pons, Johanne Mensah, Olivier Rémy-Néris, Abdeldjalil Aïssa-El-Bey, François Rousseau, Sylvain Brochar. Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning. Computers in biology and medicine. vol 171. 2024-02-13. PMID:38350399. |
we evaluated the accuracy, sensitivity, specificity, f1 score, area under the curve (auc) score, and confusion matrix of the predictions by resnet, lstm, and inceptiontime deep learning architectures for time series data. |
2024-02-13 |
2024-02-16 |
human |
| Luelue Huang, Miaoling Liu, Bin Li, Bimal Chitrakar, Xu Dua. Terahertz Spectroscopic Identification of Roast Degree and Variety of Coffee Beans. Foods (Basel, Switzerland). vol 13. issue 3. 2024-02-10. PMID:38338523. |
the classification effect and misclassification of the model were analyzed via confusion matrix. |
2024-02-10 |
2024-02-12 |
Not clear |
| Yu-Jen Fang, Chien-Wei Huang, Riya Karmakar, Arvind Mukundan, Yu-Ming Tsao, Kai-Yao Yang, Hsiang-Chen Wan. Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer. Cancers. vol 16. issue 3. 2024-02-10. PMID:38339322. |
the evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and f1-score of the trained model. |
2024-02-10 |
2024-02-12 |
Not clear |
| Chitrabhanu B Gupta, Debraj Basu, Timothy K Williams, Lucas P Neff, Michael A Johnson, Nathan T Patel, Aravindh S Ganapathy, Magan R Lane, Fatemeh Radaei, Chen-Nee Chuah, Jason Y Adam. Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data. Scientific reports. vol 14. issue 1. 2024-01-26. PMID:38278825. |
model performance was assessed using the area under the receiver operating characteristic curve (auroc), confusion matrix metrics, and calibration curves plotting predicted probabilities against observed outcomes. |
2024-01-26 |
2024-01-29 |
Not clear |
| Angela Lii, Jonathan Temte, Shari Barlow, Maureen Goss, Emily Temte, Jen Zaborek, Amra Uzicani. Assessment and comparison of the ILI case definition in clinical and school-based community settings: ORCHARDS/IISP. Annals of family medicine. vol 21. issue Suppl 3. 2024-01-25. PMID:38271207. |
relationships between influenza and each categorical variable were described by the confusion matrix, sensitivity, and specificity. |
2024-01-25 |
2024-01-28 |
human |
| Erum Yousef Abbasi, Zhongliang Deng, Arif Hussain Magsi, Qasim Ali, Kamlesh Kumar, Asma Zubed. Optimizing Skin Cancer Survival Prediction with Ensemble Techniques. Bioengineering (Basel, Switzerland). vol 11. issue 1. 2024-01-22. PMID:38247920. |
the performance was evaluated and interpreted using accuracy, precision, recall, f1 score, confusion matrix, and roc curves, where the voting method achieved a promising accuracy of 99%. |
2024-01-22 |
2024-01-24 |
Not clear |
| Xiyuan Liu, Liying Wang, Hongyan Yan, Qingjiao Cao, Luyao Zhang, Weiguo Zha. Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features. Biomimetics (Basel, Switzerland). vol 9. issue 1. 2024-01-22. PMID:38248606. |
the evaluation indicators include confusion matrix, accuracy, precision, recall rate, f1-score, and accuracy and error rate. |
2024-01-22 |
2024-01-24 |
Not clear |