Machine learning-based energy consumption models for rural housing envelope retrofits incorporating uncertainty: A case study in Jiaxian, China by Taoyuan Zhang, Zao Li, Zihuan Zhang, Yulu Chen, Xia Sun

“…Results indicate that a more uniform residual distribution within the 95 % interval balances data volume and prediction accuracy, reducing error variability and improving model performance. This study demonstrates that uncertainty-informed datasets and ML enhance the reliability and generalizability of energy consumption predictions, providing a scalable approach for optimizing rural housing energy retrofits.…”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. …”
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Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds. by Fei Cai, Yuehua Wei, Daniel Kirchhofer, Andrew Chang, Yingnan Zhang

“…Using the insights gained from the alanine scanning experiment as well as prediction model, we designed a new peptide library based on a de novo-designed HCP, which was optimized for enhanced folding efficiency. …”
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From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers by Heather Shaw, Pinkie Chambers, Matthew Watson, Luke Steventon, James Harmsworth King, Angelo Ercia, Noura Al Moubayed

“…Objectives Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model bias and data heterogeneity concerns have hampered the ability of machine learning (ML) to be deployed into clinical practice. …”
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Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models by Daniel Fernando Figueiredo Spengler, Paulo Carteri Coradi, Dágila Melo Rodrigues, Izabela Cristina de Oliveira, Dalmo Paim de Oliveira, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…Abstract The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. Thus, machine learning techniques can help optimize processes and obtain more accurate results for decision-making regarding the processing and conservation of stored seeds. …”
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Alumina-enriched sunflower bio-oil in machining of Hastelloy C-276: a fuzzy Mamdani model-aided sustainable manufacturing paradigm by Binayak Sen, Abhijit Bhowmik, Gurbhej Singh, Vishwesh Mishra, Shantanu Debnath, Rustem Zairov, Muhammad Imam Ammarullah

“…Finally, a Taguchi-designed experiment consisting of sixteen trials was performed in different lubricating conditions, and a Fuzzy-Mamdani model was employed to achieve a sustainable machining environment. …”
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Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study by Nai-Yu Kuo, Hsin-Jung Tsai, Shih-Jen Tsai, Albert C Yang

“…ObjectiveThis study aims to develop 2 sequential machine learning models to efficiently screen and differentiate OSA. …”
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Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study by Xiaoxu Wang, Yinfang Li, Zixin Cao, Yunuo Li, Jingyuan Cao, Yao Wang, Min Li, Jing Zheng, Siqi Peng, Wen Shi, Qianqian Wu, Junlan Yang, Yaping Fang, Aiqing Zhang, Xiaoliang Zhang, Bin Wang

“…Predictive factors were selected using LASSO regression combined with univariate and multivariate analyses. Machine learning models including CatBoost, XGBoost, decision tree, support vector machine, random forest, and logistic regression were used to develop the CVC risk model. …”
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An explainable radiomics-based machine learning model for preoperative differentiation of parathyroid carcinoma and atypical tumors on ultrasound: a retrospective diagnostic study by Chunrui Liu, Wenxian Li, Baojie Wen, Haiyan Xue, Yidan Zhang, Shuping Wei, Jinxia Gong, Li Huang, Jian He, Jing Yao, Zhengyang Zhou

“…The explainable bar chart, heatmap and Shapley Additive exPlanations (SHAP) values were used to explain and visualize the main predictors of the optimal model.ConclusionThis radiomics framework provides a promising tool to support doctors in the clinical management of parathyroid lesions.…”
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Enhancing Specificity in Predicting Axillary Lymph Node Metastasis in Breast Cancer through an Interpretable Machine Learning Model with CEM and Ultrasound Integration by Weimin Xu PhD, Bowen Zheng MS, Chanjuan Wen PhD, Hui Zeng MS, Sina Wang MS, Zilong He PhD, Xin Liao MS, Weiguo Chen MS, Yingjia Li PhD, Genggeng Qin PhD

“…Additionally, SHapley Additive exPlanations (SHAP) was used to interpret the optimal model. The receiver-operating characteristic curve (ROC) and AUC were used to evaluate model performance. …”
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Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System by Yongkang Li, Qing He, Yongqiang Liu, Amina Maituerdi, Yang Yan, Jiao Tan

“…We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…This online forecasting tool not only processes a large amount of data but also continuously optimizes and adjusts the accuracy of the model according to the latest medical research and clinical data. …”
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MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors by Ruiting Wang, Lianting Zhong, Pingyi Zhu, Xianpan Pan, Lei Chen, Jianjun Zhou, Yuqin Ding

“…Among the 14 machine learning classification models constructed, the combined model with LR has the highest efficiency in differentiating non-ccRCC from benign renal tumors. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. …”
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Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation by Chuanren Zhuang, Ruomeng Hu, Ke Li, Zhengshuang Liu, Songjie Bai, Sheng Zhang, Xuehuan Wen

“…BackgroundSepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.MethodsUsing the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. …”
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer by Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong

“…SHapley Additive exPlanations (SHAP) were used to interpret the optimal model and visualize the decision-making process for a single individual. …”
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Establishment of an interpretable MRI radiomics-based machine learning model capable of predicting axillary lymph node metastasis in invasive breast cancer by Dingyi Zhang, Mengyi Shen, Li Zhang, Xin He, Xiaohua Huang

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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder by Lei Tang, Liling Wu, Mengqin Dai, Nian Liu, Lu liu

“…Differentially expressed genes between MDD patients and controls were obtained from five datasets (GSE98793, GSE32280, GSE38206, GSE39653, and GSE52790), and 113 machine learning methods were employed to construct MDD diagnostic models. …”
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Construction of a risk prediction model for pulmonary infection in patients with spontaneous intracerebral hemorrhage during the recovery phase based on machine learning by Jixiang Xu, Yuan Li, Fumin Zhu, Fumin Zhu, Xiaoxiao Han, Liang Chen, Yinliang Qi, Yinliang Qi, Xiaomei Zhou, Xiaomei Zhou

“…Accurate prediction of PI risk is crucial for early intervention and optimized clinical management. The aim of this study was to develop a machine learning (ML) model for predicting PI risk in patients during the recovery phase of deep SICH and to investigate the contributions of individual risk factors through explainable artificial intelligence techniques.MethodsWe conducted a retrospective study involving 649 patients diagnosed with PI during the recovery phase of deep SICH between 2021 and 2023. …”
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Estimation of total Effort and Effort Elapsed in Each Step of Software Development Using Optimal Bayesian Belief Network by Fatemeh Zare Baghiabad, Hasan Khademi Zare, Mohammadsaber Fallahnezhad, Fazlollah Adibnia

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