Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

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Voice-Evoked Color Prediction Using Deep Neural Networks in Sound–Color Synesthesia by Raminta Bartulienė, Aušra Saudargienė, Karolina Reinytė, Gustavas Davidavičius, Rūta Davidavičienė, Šarūnas Ašmantas, Gailius Raškinis, Saulius Šatkauskas

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Enhancing prediction of fluid-saturated fracture characteristics using deep learning super resolution by Manju Pharkavi Murugesu, Vignesh Krishnan, Anthony R. Kovscek

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Sedimentary microfacies prediction based on multi-point geostatistics under the constraint of INPEFA curve by Xudong Wang, Zicheng Yang, Xibao Liu, Chengyuan Yuan

“…However, challenges persist in achieving precise stratigraphic division, sedimentary cycle characterization, and microfacies prediction. This study aims to enhance stratigraphic resolution and prediction accuracy of sedimentary microfacies to address uncertainties in sand body distribution within dense well pattern areas. …”
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Development and validation of survival prediction tools in early and late onset colorectal cancer patients by Wanling Li, Jinshan Liu, Yuntong Lan, Dongling Yu, Bingqiang Zhang

“…This study successfully developed online calculators using machine learning algorithms to predict 1- to 8-year survival probabilities for EOCRC and LOCRC patients under various treatment strategies.…”
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Development and validation of machine learning models to predict unplanned hospitalizations of patients with diabetes within the next 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitskiy, A. V. Gusev

“…The creation and inference of a machine learning model for predicting hospitalizations of patients with DM to an inpatient medical facility will make it possible to personalize the provision of medical care and optimize the load on the entire healthcare system.AIM: Development and validation of models for predicting unplanned hospitalizations of patients with diabetes due to the disease itself and its complications using machine learning algorithms and data from real clinical practice.MATERIALS AND METHODS: 170,141 depersonalized electronic health records of 23,742 diabetic patients were included in the study. …”
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Prediction and Optimization of Bolt Parameters for Automotive Fuel Cell Stack under Random Vibration by Yao Junqi, Yin Yan, Wang Bowen, Zhang Fan, Qin Zhikun, Wang Zixuan, Guo Ting

“…By integrating XGBoost with a genetic algorithm (GA), a GA-XGBoost predictive model is developed to optimize the structural parameters of the fastening bolts. …”
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Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process by Mohamed K. Mostafa, Ahmed S. Mahmoud, Mohamed S. Mahmoud, Mahmoud Nasr

“…An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal (R2: 0.972, Adj-R2: 0.971, RMSE: 1.449, and SSE: 56.680). …”
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Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation by Wanli Xiong, Qiqi Cao, Lu Jia, Min Chen, Tao Liu, Qingyan Zhao, Yanhong Tang, Bo Yang, Li Li, Shaobo Shi, He Huang, Congxin Huang, China Atrial Fibrillation Center Project Team

“…Sixty-one variables were initially included to train machine learning models, with the random forest algorithm demonstrating the best predictive performance (AUC 0.833, 95%CI 0.730–0.924). …”
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Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach by Suhas Kamshetty Chinnababu, Suhas Kamshetty Chinnababu, Suhas Kamshetty Chinnababu, Ananda Babu Jayachandra, Ananda Babu Jayachandra, Swathi Holalu Yogesh, Swathi Holalu Yogesh, Mohamed Abouhawwash, Mohamed Abouhawwash, Doaa Sami Khafaga, Eman Abdullah Aldakheel, Vinaykumar Vajjanakurike Nagaraju, Vinaykumar Vajjanakurike Nagaraju

“…Diabetes mellitus is a metabolic disorder categorized using hyperglycemia that results from the body’s inability to adequately secrete and respond to insulin. Disease prediction using various machine learning (ML) approaches has gained attention because of its potential for early detection. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Abstract The accurate prediction of compressive strength (CS) in steel fiber reinforced concrete (SFRC) remains a critical challenge due to the material’s inherent complexity and the nonlinear interactions among its constituents. …”
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Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology by Raoof Nopour

“…The chosen machine learning algorithms, including LightGBM, XG-Boost, random forest, artificial neural network, support vector machine, decision tree, K-Nearest Neighbor and logistic regression, were utilized to establish prediction models for predicting metastasis among colorectal cancer patients. …”
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The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies by Yuyan Huang, Jian Zhao, Chengxu Zheng, Chuanhui Li, Tao Wang, Liangde Xiao, Yongkuai Chen

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BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…Then, majority voting was used to build an ensemble of the top three algorithms. The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT).ResultsIn terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations.ConclusionThe results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers.…”
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Evolutionary prediction of novel biphenylene networks as an anode material for lithium and potassium-ion batteries by Adewale Hammed Pasanaje, Nirpendra Singh

“…The discovery of novel materials with compelling properties is more accessible with the help of advanced computational algorithms. Recent experimental synthesis of the biphenylene network (C6) motivated us to discover new BN-doped biphenylene networks (C4BN, C2B2N2, and B4N4) and their applications in Li(K)-ion batteries using an evolutionary algorithm and the first-principles calculations. …”
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A machine learning approach to predict phyllosphere resistome abundance across urbanization gradients by Rui-Ao Ma, Yi-Hui Ding, Shifa Zhong, Ting-Ting Jing, Xuechu Chen, Si-Yu Zhang

“…Recent studies reported an increased abundance of antibiotic resistance genes (ARGs) in urban greenspaces, yet the predictability of ARG variance along urbanization gradients remains unclear. …”
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Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model by Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy

“…These results indicate that our proposed technique achieved a high prediction model for liver disease.…”
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An enhanced alpha evolution moss growth optimizer for prognostic prediction in spontaneous intracerebral hemorrhage by Lingxian Hou, Yongsheng Wang, Xiuqi Lin, Chengye Li, Huangrong Guo, Congcong Jin, Yi Chen, Huiling Chen, Jing Ji, Wenzong Zhu

“…This study aims to improve SICH outcome prediction by developing the Alpha Evolution Moss Growth Optimization (AEMGO) algorithm for feature selection in high-dimensional medical datasets. …”
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A QSAR-based application for the prediction of lethal blood concentration of new psychoactive substances by Tarcisio Correa, Jéssica Sales Barbosa, Thiara Vanessa Barbosa da Silva, Thiala Soares Josino da Silva Parente, Danielle de Paula Magalhães, Wanderley Pinheiro Holanda Júnior

“…To strengthen forensic interpretation of NPS intoxication cases, we have developed a predictive model for estimating human lethal blood concentrations (LBC) of various NPS. …”
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Predicting ICU mortality in heart failure patients based on blood tests and vital signs by Yeao Wang, Jianke Rong, Zhili Wei, Xiaoyu Bai, YunDan Deng

“…BackgroundCurrently, heart failure has become one of the major complications in the advanced stages of various cardiovascular diseases. Numerous predictive models have been developed to estimate the mortality rate of heart failure patients; however, these models often require the measurement of multiple indicators and the inclusion of various scoring systems. …”
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