Ocular biometric parameters in Chinese preschool children and physiological axial length growth prediction using machine learning algorithms: a retrospective cross-sectional study by Yan Li, Kai Wang, Tao Tang, Heng Zhao, Jingwei Zhou, Mingwei Zhao, Duanke Liu, Xuewei Li, Xiaoqing Shi, Jiahui Ma, Chenxu Zhao

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Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study. by Lorenzo Ruinelli, Pietro Cippà, Chantal Sieber, Clelia Di Serio, Paolo Ferrari, Antonio Bellasi

“…The negative predictive value (NPV) progressively increased from 94% to 98% consistently. …”
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Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer by FangHao Cai, Zhengjun Guo, GuoYu Wang, FuPing Luo, Yang Yang, Min Lv, JiMin He, ZhiGang Xiu, Dan Tang, XiaoHui Bao, XiaoYue Zhang, ZhenZhou Yang, Zhi Chen

“…Abstract Objectives To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models using various machine learning algorithms to identify the optimal model, and integrate clinical factors to establish a nomogram for predicting the therapeutic response to induction therapy(IT) in locally advanced non-small cell lung cancer. …”
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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

“…Then, characteristics, such as color, spectral reflectance, vegetation indices (VIs), rehydration rate (RR), drying kinetics, moisture ratio (MR), and moisture content (MC) were measured and compared after using the above-mentioned drying methods. Also, the MR was predicted by the MC, and the drying rate (DR), drying times, and final thickness were predicted using the multi-layer perceptron (MLP), gaussian process (GP), k-nearest neighbors (KNN), random forest (RF), and support vector regression (SVR) algorithms. …”
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Prediction of one-year recurrence among breast cancer patients undergone surgery using artificial intelligence-based algorithms: a retrospective study on prognostic factors by Raoof Nopour

“…So far, Artificial intelligence algorithms integrated with various clinical data have demonstrated potential predictive capability regarding breast cancer recurrence. …”
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Short-term salinity prediction for coastal areas of the Vietnamese Mekong Delta using various machine learning algorithms: a case study in Soc Trang Province by Le Thi Thanh Dang, Hiroshi Ishidaira, Ky Phung Nguyen, Kazuyoshi Souma, Jun Magome

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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms by S. Ajith, S. Vijayakumar, N. Elakkiya

“…This review synthesizes advancements in artificial intelligence applications across key domains, including crop yield prediction, precision irrigation, soil fertility mapping, insect pest and disease forecasting, and foodgrain quality assessment. …”
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Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms by Wenzhi Wang, Feng Jin, Lina Song, Jinfang Yang, Yingjian Ye, Junjie Liu, Lei Xu, Peng An

“…Abstract Objectives This study aimed to develop a model for predicting peripheral lymph node metastasis (LNM) in thyroid cancer patients by combining enhanced CT radiomic features with machine learning algorithms. …”
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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Furthermore, this study will evaluate and compare the performance metrics of the 4 different ML algorithms, and the best model will be used to develop an HIV testing predictive model. …”
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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

“…The study objective is to build a model based on machine learning that can predict the detection of SARS-CoV-2 from medical data. …”
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From multi-omics to predictive biomarker: AI in tumor microenvironment by Luo Hai, Luo Hai, Ziming Jiang, Haoxuan Zhang, Yingli Sun, Yingli Sun, Yingli Sun

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The Improved-EFI Score: A Multi-Omics-Based Novel Efficacy Predictive Tool for Predicting the Natural Fertility of Endometriosis Patients by He Q, Zhang C, Hu Y, Deng J, Zhang S

“…An improved endometriosis fertility index (EFI) predictive model was created based on ultrasound radiomics and urinary proteomics gathered during the patient’s initial admission, using two machine learning algorithms. …”
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Designing Predictive Tools for Personalized Functionalities in Knitted Performance Wear by Martijn ten Bhömer, Hai-Ning Liang, Difeng Yu, Yuanjin Liu, Yifan Zhang, Eva de Laat, Carola Leegwater

“…(2) How to design interactions and interfaces that use intelligent predictive algorithms to stimulate creativity during the fashion design process? …”
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Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool by Hatem Ali, Arun Shroff, Tibor Fülöp, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. …”
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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…Abstract Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. …”
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Research on Thickness Error Optimization Method of Rolling System Based on Improved Sparrow Search Algorithm–Bidirectional Long Short-Term Memory Network–Attention by Qingyun Wu, Xinchen Li, Jiafei Ji, Bowen Xing

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A predictive analytics framework for opportunity sensing in stock market by Shruti Mittal, C.K. Nagpal

“… Large volume, random fluctuations and distractive patterns in raw price data lead to overfitting in stock price prediction. Thus research papers in this area suffer from multiple limitations: Very short prediction period from one day to one week, consideration of few stocks only instead of whole of stock market spectrum, exploration of more suitable machine learning algorithms. …”
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Generative and predictive neural networks for the design of functional RNA molecules by Aidan T. Riley, James M. Robson, Aiganysh Ulanova, Alexander A. Green

“…Here we present a generalized, efficient neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions across a diverse range of settings. We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. …”
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Robust Predictive Maintenance for Robotics via Unsupervised Transfer Learning by Arash Golibagh Mahyari, Thomas locher

“…In this paper, we propose a novel solution based on transfer learning which addresses a well-known challenge in predictive maintenance algorithms by passing the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate such false alarms. …”
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Self-supervised predictive learning accounts for cortical layer-specificity by Kevin Kermani Nejad, Paul Anastasiades, Loreen Hertäg, Rui Ponte Costa

“…Inspired by self-supervised learning algorithms, we propose a computational theory in which layer 2/3 (L2/3) integrates past sensory input, relayed via layer 4, with top-down context to predict incoming sensory stimuli. …”
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