Suchergebnisse - Predicting the arrest using Random forest Algorithm~

Crime data analysis and prediction of arrest using machine learning

Autoren: Sankul, Revanth Aruva, Tejaswi Reddy Kankal, Sai Varun et al.

Quelle: World Journal of Advanced Research and Reviews. 25:498-506

Schlagwörter: Machine Learning, Predicting the arrest using Random forest Algorithm, Crime data Analysis, Statistical methods, User friendly stream lit interface, Crime Reports

Intelligent Prognosis and Severity Analysis of Coronary Artery Disease.

Autoren: J, Janani Meghana, B P, Ramu Keerthana et al.

Quelle: Procedia Computer Science. 2025, Vol. 258, p1123-1130. 8p.

Schlagwörter: Coronary artery disease, Random forest algorithms, Decision trees, Heart failure, Cardiac arrest

Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning.

Autoren: Youngnam Lee Joon-myoung Kwon Yeha Lee et al.

Quelle: Acute & Critical Care; Aug2018, Vol. 33 Issue 3, p117-120, 4p

Schlagwörter: DEEP learning, CARDIAC arrest, MACHINE learning, ELECTRONIC health records, RANDOM forest algorithms

Enhanced Fault Diagnosis Method for Tractors Using Random Forest Algorithm.

Autoren: Mohebbi, Milad Mohebbi, Yalda

Quelle: Advances in Engineering & Intelligence Systems; Sep2025, Vol. 4 Issue 3, p57-70, 14p

Predicting Bone Cement Implantation Syndrome Using Artificial Intelligence Methods

Weitere Verfasser: Muhammed Cagatay Engin, associate professor doctor

Quelle: Predicting the Development of Bone Cement Implantation Syndrome in Arthroplasty Operations Using Artificial Intelligence Methods

Evaluation and Optimization of Telephone Triage Using Artificial Intelligence (AI) Models for the Detection of Demands for Time-dependent Pathology at the Emergency and Urgent Care Coordination Center (CCUE). (TrIAje Project)

Autoren: Junta de Andalucia Andaluz Health Service

Quelle: Proyecto "trIAje": evaluación y optimización Del Triaje telefónico Mediante Modelos de Inteligencia Artificial (IA) Para la detección de Demandas Por patología Tiempo-dependiente en el Centro Coordinador de Urgencias y Emergencias (CCUE).

ECG-based pulse detection during cardiac arrest using random forest classifier.

Autoren: Elola, Andoni Aramendi, Elisabete Irusta, Unai et al.

Quelle: Medical & Biological Engineering & Computing; Feb2019, Vol. 57 Issue 2, p453-462, 10p, 4 Charts, 4 Graphs

Schlagwörter: ELECTROCARDIOGRAPHY, CARDIAC arrest, CARDIOPULMONARY resuscitation, PULSE (Heart beat), AUTOMATED external defibrillation, ALGORITHMS, HEART beat

DEVELOPMENT OF PREDICTION MODEL OF STEEL FIBER-REINFORCED CONCRETE COMPRESSIVE STRENGTH USING RANDOM FOREST ALGORITHM COMBINED WITH HYPERPARAMETER TUNING AND K-FOLD CROSS-VALIDATION.

Autoren: Al-Abdaly, Nadia Moneem Al-Taai, Salwa R. Imran, Hamza et al.

Quelle: Eastern-European Journal of Enterprise Technologies; 2021, Vol. 113 Issue 7, p59-65, 7p

Schlagwörter: FIBER-reinforced concrete, RANDOM forest algorithms, STANDARD deviations, PREDICTION models, STEEL

Prediction of cardiac attack using ensemble learning.

Autoren: Eranki, Kiran L. N. Kalerua, Sai Chandu Pilli, Tejaswi et al.

Quelle: AIP Conference Proceedings; 2024, Vol. 2971 Issue 1, p1-9, 9p

Schlagwörter: MACHINE learning, DIETARY patterns, SUPPORT vector machines, K-nearest neighbor classification, FEATURE selection, RANDOM forest algorithms, DATA privacy, MYOCARDIAL infarction, HUMAN fingerprints

Machine Learning-Based Cardiac Arrest Prediction for Early Warning System.

Autoren: Chae, Minsu Gil, Hyo-Wook Cho, Nam-Jun et al.

Quelle: Mathematics (2227-7390); Jun2022, Vol. 10 Issue 12, p2049-N.PAG, 17p

Schlagwörter: CARDIAC arrest, DEEP learning, MACHINE learning, RANDOM forest algorithms, DECISION trees, LOGISTIC regression analysis, FALSE alarms

Using Machine Learning Algorithms in Medication for Cardiac Arrest Early Warning System Construction and Forecasting

Autoren: Cheng-Tse Wu Hsiao-Ko Chang Ji-Han Liu et al.

Quelle: 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI). :1-4

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 3. Good health

Zugangs-URL: https://dblp.uni-trier.de/db/conf/taai/taai2018.html#ChangWLJ18

Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study

Autoren: Jussi Pirneskoski Joonas Tamminen Antti Kallonen et al.

Quelle: Resuscitation Plus, Vol 4, Iss , Pp 100046- (2020)

Schlagwörter: Emergency medical services, Prehospital, Cardiac arrest prevention, Early warning score, National Early Warning Score, NEWS, Specialties of internal medicine, RC581-951

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666520420300461; https://doaj.org/toc/2666-5204

Zugangs-URL: https://doaj.org/article/629368b162684190955e9f9478bece7f

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Autoren: Joon‐myoung Kwon Youngnam Lee Yeha Lee et al.

Quelle: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 7, Iss 13 (2018)

Schlagwörter: artificial intelligence, cardiac arrest, deep learning, machine learning, rapid response system, resuscitation, Diseases of the circulatory (Cardiovascular) system, RC666-701

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2047-9980

Zugangs-URL: https://doaj.org/article/8016cec78ea048a8976e8c8a3d8fec81

Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning.

Autoren: Chae, Minsu Han, Sangwook Gil, Hyowook et al.

Quelle: Diagnostics (2075-4418); Jul2021, Vol. 11 Issue 7, p1255, 1p

Schlagwörter: CARDIAC arrest, DEEP learning, RANDOM forest algorithms, DECISION trees, LOGISTIC regression analysis

Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A singlecenter retrospective cohort study

Autoren: Cheungpasitporn, W Ueno, R Xu, L et al.

Index Begriffe: Journal Article

URL: https://hdl.handle.net/11343/251520

The Core-Targeted RRM2 Gene of Berberine Hydrochloride Promotes Breast Cancer Cell Migration and Invasion via the Epithelial–Mesenchymal Transition.

Autoren: He, Jiaming Wei, Qiang Jiang, Rong et al.

Quelle: Pharmaceuticals (14248247); Jan2023, Vol. 16 Issue 1, p42, 19p

Schlagwörter: EPITHELIAL-mesenchymal transition, BERBERINE, RANDOM forest algorithms, CANCER cell migration, BREAST cancer, CELL migration, GENE expression

Random-forest algorithm based biomarkers in predicting prognosis in the patients with hepatocellular carcinoma.

Autoren: Guo, Lingyun Wang, Zhenjiang Du, Yuanyuan et al.

Quelle: Cancer Cell International; 6/17/2020, Vol. 20 Issue 1, p1-12, 12p

Schlagwörter: HEPATOCELLULAR carcinoma, IMMUNOSTAINING, SUPERVISED learning, RNA sequencing, DIGESTIVE organs

Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest.

Autoren: Yin, Fufen Shao, Xingyang Zhao, Lijun et al.

Quelle: Oncology Letters; Aug2019, Vol. 18 Issue 2, p1597-1606, 10p

Schlagwörter: GENE expression, ADENOCARCINOMA, LOG-rank test, PROGNOSIS, RNA sequencing

Firma/Körperschaft: PEKING University (Beijing, China)

Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea.

Autoren: Kim, Ji Woong Ha, Juhyung Kim, Taerim et al.

Quelle: Journal of Medical Internet Research. Jul2021, Vol. 23 Issue 7, pN.PAG-N.PAG. 1p. 2 Charts.

Schlagwörter: *Artificial neural networks, Prediction models, Cardiac arrest, Receiver operating characteristic curves, Random forest algorithms, Medical personnel

Geografische Kategorien: South Korea

Targeting domestic abuse by mining police records

Autoren: Bland, Matthew Paul Ariel, Barak

Weitere Verfasser: Bland, Matthew Paul Ariel, Barak

Schlagwörter: 363.2, Domestic Abuse, Police Data, Random Forest

Zugangs-URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793089