Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations by Anders Björkelund, Mattias Ohlsson, Jakob Lundager Forberg, Arash Mokhtari, Pontus Olsson de Capretz, Ulf Ekelund, Jonas Björk

“…The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high‐sensitivity cardiac troponin T (hs‐cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI. …”
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Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study by Tao Thi Tran, Jeonghee Lee, Junetae Kim, Sun-Young Kim, Hyunsoon Cho, Jeongseon Kim

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Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study by Jialu Li, Yi Ding, Yiwei Hao, Chengyu Gao, Jinjing Xiao, Ying Liu, Yining Zhao, Qinlan Li, Lulu Xing, Hongyuan Liang, Liang Ni, Fang Wang, Sa Wang, Di Yang, Guiju Gao, Jiang Xiao, Hongxin Zhao

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Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients by Yuan Liu, Yuankun Liu, Yu Zhang, Pengpeng Zhang, Jiaheng Xie, Ning Zhao, Yi Xie, Chao Cheng, Songyun Zhao

“…The AUC value for the external validation set was 0.93, indicating robust extrapolative capabilities of the XGBoost prediction model. The HF prediction model post-CME, derived from the XGBoost machine learning algorithm in this study, attests to its elevated predictive accuracy and clinical utility.…”
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Development of a prediction model for chemotherapy and immunotherapy response in esophageal squamous cell carcinoma patients using machine learning algorithms by CHEN Jincheng, ZHANG Xiaoqin, LIU Jie

“…Objective‍　‍To develop models for predicting response to chemotherapy combined with immunotherapy in patients with esophageal squamous carcinoma with various machine learning algorithms, and then select the optimal model. …”
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Development of a prediction model for acute respiratory distress syndrome in ICU patients with acute pancreatitis based on machine learning algorithms by REN Xia*，LIU Luojie，ZHA Junjie，YE Ye，XU Xiaodan，YE Hongwei，ZHANG Yan

“…"Objective To develop and validate a predictive model based on machine learning algorithms to assess the risk of acute respiratory distress syndrome（ARDS）in patients with acute pancreatitis（AP）admitted to the intensive care unit（ICU）. …”
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Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine by Hojin Moon, Lauren Tran, Andrew Lee, Taeksoo Kwon, Minho Lee

“…Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. …”
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Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

“…Furthermore, the six machine learning algorithms consistently identified GGT and ALT as the most significant predictive features. …”
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Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum by Maryam Mazaheri-Tirani, Soleyman Dayani, Majid Iranpour Mobarakeh

“…All levels of ZnO NPs treatments increased growth parameters compared to the control. All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. …”
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Analyzing Agricultural Land Price Prediction Using Linear Regression and XGBoost Machine Learning Algorithms: A Case Study of Çanakkale by Simge Doğan, Levent Genç, Sait Can Yücebaş, Metin Uşaklı

“…This study aims to compare the MLR and XGBoost algorithms to predict agricultural land prices in villages located in the central district of Çanakkale and to examine daily fluctuations in economic indicators such as the dollar, gold, and euro. …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. …”
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Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning by Loke Kok Foong, Vojtech Blazek, Lukas Prokop, Stanislav Misak, Farruh Atamurotov, Nima Khalilpoor

“…Future research should explore additional optimisation algorithms and ensemble techniques to improve prediction robustness and accuracy. …”
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Toward prediction of entrepreneurial exit in Iran; a study based on GEM 2008-2019 data and approach of machine learning algorithms by Masoumeh Moterased, Seyed Mojtaba Sajadi, Ali Davari, Mohammad Reza Zali

“…This research applies the Random Forest Algorithm to get a prediction model that shows the entrepreneurial exit. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…This study presents a dynamic continuous error compensation model for direct-drive turntables, based on an analysis of positioning error mechanisms and the implementation of a “decomposition-modeling-integration-correction” strategy, which features high flexibility, adaptability, and online prediction-correction capabilities. Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk by Mohamed Chaouch, Omama M. Al-Hamed

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Transformer–BiLSTM Fusion Neural Network for Short-Term PV Output Prediction Based on NRBO Algorithm and VMD by Xiaowei Fan, Ruimiao Wang, Yi Yang, Jingang Wang

“…And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. …”
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Prediction of Chlorophyll-a Concentration Distribution Using Generative Adversarial Network (GAN) Algorithm on Processed Remote Sensing Images by Wattaqwa Amira Zul’Ilmi, Jaya Indra, Iqbal Muhammad

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A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters by Murat Canayaz, Murat Demir, Zeynal Topalcengiz

“…Recently, the performances of various algorithms have been tested for the prediction of indicator bacteria population and pathogen occurrence in agricultural water sources. …”
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Bayesian optimization with Optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization by Bamidele A. Dada, Nnamdi I. Nwulu, Seun O. Olukanmi

“…Optuna's tree-structured Parzen estimator (TPE) and pruning algorithms are employed to generate more precise estimates of soil nutrients. …”
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Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering by Senhui Wang

“…The results showed that an improvement in predictive accuracy can be obtained by the GA-ELM approach, and the accuracy of TI prediction is 81.85% for absolute error under 0.7%.…”
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