Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…This study examines the application of advanced machine learning techniques, combined with metaheuristic optimization methods, to improve predictive models for intradialytic hypotension (IDH) in hemodialysis patients. …”
Get full text

A Predicting Method of the Strong Cooling Process during Winter with Numerical Model Prediction and its Operational Application by Bomin CHEN, Kun ZHOU, Fei XIN, jun MA, Limei JIN

“…At first this paper quantitatively evaluated the low-frequency wave performance of NCEP-CF Sv2 model over the eight key areas on 700 hPa from January to March and from October to December of 2017， and then made operationally the fifteen extended-range operational predictions of strong cooling process for January to April of 2018 and for November to January 2019 with the 1~30 days prediction given by CFSv2 model as well as the low-frequency wave conceptual predicting model.The results show that the phase and evolution trend of the low-frequency wave in the key area predicted by CFSv2 model are highly consistent with the reality， with the correlation coefficients of 0.839 of the predicted low-frequency waves with the observed for the extended-range （11~30 days）， the accuracy of low frequency wave trend by the model over 3~6 pentad up to percent of 83.3 on average， and the percentage of 100-percent accuracy of the trend even up to 45.8.The average accuracy， Cs and Zs scores of 15 strong cooling process operational predicting are 61.2%， 0.149 and 0.158 respectively， and at the same time the occurrence of the two strongest cooling processes at the beginning and the end of 2018 were accurately given with the lead-time of 18 and 16 days in turn， which are significantly higher than those of the operation predicting for the same period of 2015 to 2017 without CFSv2 results.…”
Get full text

Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer by Yingkun Xu, Dan Shu, Meiying Shen, Qiulin Wu, Yang Peng, Li Liu, Zhenrong Tang, Shun Gao, Yuan Wang, Shengchun Liu

“…Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. …”
Get full text

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

Get full text

Flare Set-Prediction Transformer: A Transformer-Based Set-Prediction Model for Detailed Solar Flare Forecasting by Liang Qiao, Gang Qin

“…Solar flare prediction models typically use classification, predicting only the probability of categorized events within a time window. …”
Get full text

ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction by Yalan Li, Haiming Deng, Jian Xiao, Bin Li, Tao Han, Jianquan Huang, Haijun Liu

“…Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. …”
Get full text

Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study by Ji Yoon Kim

“…Purpose This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. …”
Get full text

Prediction of 28-day mortality in patients with sepsis based on a predictive model: A retrospective cohort study by Yi Sun, Tingting Wang, Mengna Zhang, Shuchen Cao, Liwei Hua, Kun Zhang

“…Objective This study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit. …”
Get full text

Radiographic prediction model based on X-rays predicting anterior cruciate ligament function in patients with knee osteoarthritis by Guanghan Gao, Yaonan Zhang, Lei Shi, Lin Wang, Fei Wang, Qingyun Xue

Subjects: Get full text

External validation of the MR PREDICTS@24H model: predicting functional outcome after endovascular treatment in stroke by Michael Sonnberger, Raimund Helbok, Jeanette Tas, Milan R Vosko, Cristina Cerinza Sick, Caterina Kulyk, Bogdan-Andrei Ianosi, Patrizia Spiandorello, Melanie Bergmann

“…Background Chalos et al recently developed the MR PREDICTS@24H model to predict 90 days functional outcomes in ischaemic stroke patients following endovascular treatment (EVT). …”
Get full text

Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model by Iolanda Ferreira, Joana Simões, João Correia, Ana Luísa Areia

Subjects: Get full text

Bleeding risk and prediction model to predict bleeding risk of tissue biopsy for definitive diagnosis in patients with suspected amyloidosis by Watsamon Uraiwan, Pirun Saelue

Subjects: Get full text

Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques by Muzammil Khan, Azmat Ali, Yasser Alharbi

“…The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. …”
Get full text

Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction. by Ruiyu Zhan

“…Regarding precision, the model achieved accuracies of 0.67, 0.69, and 0.66 for miRNA expression, gene expression, and DNA methylation, respectively. …”
Get full text

Statin effects on the lipidome: Predicting statin usage and implications for cardiovascular risk prediction by Changyu Yi, Kevin Huynh, Yvette Schooneveldt, Gavriel Olshansky, Amy Liang, Tingting Wang, Habtamu B. Beyene, Aleksandar Dakic, Jingqin Wu, Michelle Cinel, Natalie A. Mellett, Gerald F. Watts, Joseph Hung, Jennie Hui, John Beilby, Joanne E. Curran, John Blangero, Eric K. Moses, John Simes, Andrew M. Tonkin, Leonard Kritharides, David Sullivan, Jonathan E. Shaw, Dianna J. Magliano, Agus Salim, Corey Giles, Peter J. Meikle, A. Tonkin, P. Aylward, D. Colquhoun, P. Glasziou, P. Harris, D. Hunt, A. Keech, S. MacMahon, P. Magnus, D. Newel, P. Nestel, N. Sharpe, J. Shaw, R.J. Simes, P. Thompson, A. Thomson, M. West, H. White, A. Thomson, S. Simes, D. Colquhoun, W. Hague, S. MacMahon, R.J. Simes, R.J. Simes, P. Glasziou, S. Caleo, J. Hall, A. Martin, S. Mulray, P. Barter, L. Beilin, R. Collins, J. McNeil, P. Meier, H. Willimott, P. Harris, W. Hague, D. Smithers, A. Tonkin, P. Wallace, H. Willimott, D. Hunt, J. Baker, P. Aylward, P. Harris, M. Hobbs, P. Thompson, N. Sharpe, D. Hunt, M. West, P. Thompson, H. White, P. Aylward, D. Colquhoun, D. Sullivan, A. Keech, P. Thompson, S. MacMahon, A. Tonkin, M. West, H. White, N. Anderson, G. Hankey, R.J. Simes, S. Simes, J. Watson, R.J. Simes, N. Sharpe, A. Thomson, A. Tonkin, H. White, W. Hague, J. Baker, M. Arulchelvam, S. Chup, J. Daly, J. Hanna, A. Leach, M. Lee, J. Loughhead, H. Lundie-Jenkin, J. Morrison, A. Martin, S. Mulray, S. Netting, A. Nguyen, H. Pater, R. Philip, G. Pinna, D. Rattos, S. Ryerson, V. Sazhin, S. Simes, R. Walsh, A. Keech, R.J. Simes, A. Clague, M. Mackie, J. Yallop, K. Boss, S. MacMahon, M. Whiting, M. Shepard, J. Leach, M. Gandy, J. Joughin, J. Seabrook, R. Abraham, J. Allen, F. Bates, I. Beinart, E. Breed, D. Brown, N. Bunyan, D. Calvert, T. Campbell, D. Condon-Paoloni, B. Conway, L. Coupland, J. Crowe, N. Cunio, B. Cuthbert, N. Cuthbert, S. D’Arcy, P. Davidson, B. Dwyer, J. England, C. Friend, G. Fulcher, S. Grant, K. Hellestrand, M. Kava, L. Kritharides, D. McGill, H. McKee, A. McLean, M. Neaverson, G. Nelson, M. O’Neill, C. Onuma, F. O’Reilly, A. Owensby, D. Owensby, J. Padley, G. Parnell, S. Paterson, C. Pawsey, R. Portley, K. Quinn, D. Ramsay, M. Russell, J. Ryan, B. Sambrook, L. Shields, J. Silberberg, S. Sinclair, D. Sullivan, P. Taverner, D. Taylor, M. Taylor, M. Threlfall, J. Turner, A. Viles, J. Waites, R. Walker, W. Walsh, K. Wee, P. West, R. Wikramanayake, D. Wilcken, J. Woods, R.K. Barnett, Z. Bogetic, H. Briggs, A. Broughton, L. Brown, A. Buncle, P. Calafiore, L. Carrick, Y. Cavenett, L. Champness, R. Clark, H. Connor, J. Counsell, J. Deague, G. Derwent-Smith, A. Driscoll, B. Feldtmann, L. Fisher, B. Forge, A. Hamer, H. Harrap, S. Hodgens, M. Hooten, J. Hurley, B. Jackson, J. Johns, J. Krafchek, H. Larwill, I. Lyall, S. Marks, M. Martin, B. Mason, J. McCabe, C. Medley, L. Morgan, L. Mullan, D. Ogilvy, G. Phelps, P. Phillips, H. Prendergast, D. Rose, G. Rudge, W. Ryan, M. Sallaberger, G. Savige, B. Sia, A. Soward, C. Steinfort, K. Tankard, J. Tippett, B. Tyack, J. Voukelatis, M. Wahlqvist, N. Walker, S. Whitten, R. Yee, M. Zanoni, R. Ziffer, K. Anderson, G. Aroney, C. Atkinson, K. Boyd, R. Bradfield, G. Cameron, D. Careless, A. Carle, P. Carroll, T. Carruthers, D. Chaseling, B. Cooke, S. Coverdale, B. Currie, M. d’Emden, F. Ekin, R. Elder, T. Elsley, L. Ferry, C. Gnanaharan, K. Graham, K. Gunawardane, C. Hadfield, C. Halliday, R. Halliday, A. Heyworth, B. Hicks, P. Hicks, T. Htut, L. Hughes, J. Humphries, H. LeGood, J. Nye, D. O’Brien, G. Real, K. Roberts, L. RossLee, J. Sampson, I. Scott, H. Smith, V. Smith-Orr, Y. Tan, B. Wicks, J. Wicks, S. Woodhouse, J. Bradley, L. Callaway, A. Calvert, J. Crettenden, A. Dufek, B. Dunn, C. Dunphy, D. Gow, I. Hamilton-Craig, K. Herewane, S. Keynes, L. McLeay, R. McLeay, L. Ng, C. Thomas, P. Tideman, L. Wilson, R. Yeend, C. Zhang, Y. Zhang, P. Bradshaw, M. Brooks, R. Burton, J. Garrett, K. Gotch-Martin, J. Hargan, B. Hockings, G. Lane, S. Ross, R. Cutforth, D. D’Silva, W. Hitchener, V. Kimber, G. Kirkland, P. Neid, R. Parkes, B. Singh, C. Singh, M. Smith, S. Smith, M. Templer, N. Whitehouse, R. Allen-Narker, R. Anandaraja, S. Anandaraja, P. Barclay, S. Baskaranathan, P. Bridgman, J. Brown, J. Bruning, J. Calton, A. Clague, M. Clark, D. Clarke, T. Cook, R. Coxon, M. Denton, A. Doone, R. Easthope, J. Elliott, C. Ellis, P. FosterPratt, C. Frenneux, M. Frenneux, D. Friedlander, D. Fry, L. Gibson, M. Gluyas, A. Hall, K. Hall, A. Hamer, H. Hart, P. Healy, J. Hedley, P. Heuser, H. Ikram, D. Jardine, J. Kenyon, H. King, T. Kirk, T. Lawson, P. Leslie, G. Lewis, E. Low, R. Luke, S. Mann, D. McClean, D. McHaffie, L. Nairn, H. Patel, L. Pearce, K. Ramanathan, R. Rankin, J. Reddy, S. Reuben, R. Ronaldson, D. Roy, H. Roy, P. Scobie, D. Scott, J. Scott, K. Skjellerup, R. Stewart, D. Walters, T. Wilkins, A. Vitanachy, P. Wright, A. Zambanini

“…We demonstrated that the re-weighted models achieved comparable prediction accuracy to ad hoc models which use the aligned predictor between training and target data. …”
Get full text

Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control by Rongjun Cheng, Haoli Lou, Qi Wei

Subjects: “…model predictive control…”
Get full text

PREDICTIVE ASSESSMENT OF TECHNICAL MEANS OF FEED GRAIN PROCESSING FOR LIVESTOCK by Dmitry I. Chistyakov

“…A process model for forming technical-and-economic indices in estimating mechanical equipment for feed grain processing for livestock is offered. …”
Get full text

Early prediction of survival at different time intervals in sepsis patients: A visualized prediction model with nomogram and observation study by Shih-Hong Chen, Yi-Chia Wang, Anne Chao, Chih-Min Liu, Ching-Tang Chiu, Ming-Jiuh Wang, Yu-Chang Yeh

“…A Cox proportional hazard model was used to analyze the survival data and determine significant risk factors to develop a prediction model. …”
Get full text

On Woven Fabric Sound Absorption Prediction by Iwan PRASETIYO, Gradi DESENDRA, Melissa N. HERMANTO, Damar R. ADHIKA

Subjects: Get full text

Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction by Yupeng Li, Ke Sun

“…Predictive analytics are advanced through the utilization of sophisticated machine learning methodologies, specifically the Random Forest classification model (RFC), The Grasshopper Optimizer Algorithm (GOA), and the Artificial Rabbits Optimizer (ARO). …”
Get full text