An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Afterwards, the Shapley Additive exPlanations (SHAP) method is applied for feature selection and dimensionality reduction, providing detailed explanations of each feature’s contribution to classification performance. …”
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A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…Based on the data spanning from 2005 to 2016 obtained from DMSP/SSUSI, we explore several machine learning algorithms, such as KNN, RF, and XGBoost, to construct an auroral oval prediction model. …”
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A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction by Shrinithi Natarajan, Niveditha Kundapuram, Nisarga Bhaskar, Sai Sailaja Policharla, Bhaskarjyoti Das

“…The proposed method outperforms existing state-of-the-art tools such as Phenomizer and GCN, in terms of both prediction accuracy and processing speed. Notably, it captures 50% of causal genes within the top 10 predictions and 85% within the top 100 predictions and the algorithm maintains a high accuracy rate of 98.09% for the top-ranked gene. …”
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Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms in classify participants by age, number of falls and falls frequency based on features extracted from a public database of stabilometric assessments. 163 participants (116 women and 47 men) between 18 and 85 years old, 44.0 to 75.9 kg mass, 140.0 to 189.8 cm tall, and 17.2 to 31.9 kg/m2 body mass index. …”
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Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. …”
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The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases by Bo Gao, Wendu Duan

“…With the rapid progress of artificial intelligence (AI) technology, its applications in the medical field, particularly in the prediction, diagnosis, and treatment of liver diseases, have drawn increasing attention. …”
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AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications by Xiaoya Chen, Huinan Xu, Shengjie Yu, Wan Hu, Zhongjin Zhang, Xue Wang, Yue Yuan, Mingyue Wang, Liang Chen, Xiumei Lin, Yinlei Hu, Pengfei Cai

“…Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. …”
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A portable retina fundus photos dataset for clinical, demographic, and diabetic retinopathy prediction by Chenwei Wu, David Restrepo, Luis Filipe Nakayama, Lucas Zago Ribeiro, Zitao Shuai, Nathan Santos Barboza, Maria Luiza Vieira Sousa, Raul Dias Fitterman, Alexandre Durao Alves Pereira, Caio Vinicius Saito Regatieri, Jose Augusto Stuchi, Fernando Korn Malerbi, Rafael E. Andrade

“…To validate the utility of mBRSET, state-of-the-art deep models, including ConvNeXt V2, Dino V2, and SwinV2, were trained for benchmarking, achieving high accuracy in clinical tasks diagnosing diabetic retinopathy, and macular edema; and in fairness tasks predicting education and insurance status. The mBRSET dataset serves as a resource for developing AI algorithms and investigating real-world applications, enhancing ophthalmological care in resource-constrained environments.…”
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Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…Stroke is reported to be one of the major causes of death and this can be reduced by studying the risk factors causing it and predicting its occurrence so as to educate people about it. …”
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Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…Objectives To compare model performance and predictive accuracy of classic regressions and machine learning approaches using data from the Inspiring Families programme. …”
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Predicting Credit Card Fraud using Supervised Machine Learning Methods: Comparative Analysis by Güner Altan, Metin Recep Zafer

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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

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NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…In the literature, RGB camera-based NDVI prediction studies involving machine learning and deep learning algorithms have focused on the correlation of the results with the reference data (R²) or the model accuracy of the algorithms used. …”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…The purpose of the current study was to develop and validate a clinical risk model to predict malignancy in patients with thyroid nodules.   …”
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Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Abstract Study objectives This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model’s predictions. …”
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Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation by B. Shamreen Ahamed, Meenakshi S. Arya, Auxilia Osvin V. Nancy

“…There are many algorithms that have played a critical role in the prediction of diseases. …”
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Screening of potential markers for vitiligo based on bioinformatics and LASSO regression and prediction of Chinese medicine by Wei Liang, Minni Huang, Yue Sun, Shuyu Guan

“…Immune cell infiltration analysis showed that these four key genes had high expression in immune cells. The prediction results of traditional Chinese medicine showed that 15 traditional Chinese medicines were related to the key genes. …”
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Development and validation of a nomogram for predicting low Kt/Vurea in peritoneal dialysis patients by Danfeng Zhang, Tian Zhao, Liting Gao, Huan Zhu, Haowei Jin, Guiling Liu, Deguang Wang

“…Abstract Background This study aimed to develop a nomogram to predict peritoneal dialysis (PD) adequacy in incident PD patients and identify those at high risk for low Kt/Vurea PD function. …”
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Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez, Ricardo Chaparro-Sánchez

“…To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. …”
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Parametric BIM and Machine Learning for Solar Radiation Prediction in Smart Growth Urban Developments by Seongchan Kim, Jong Bum Kim

“…The simulation results were then used to create ML models for context-specific solar radiation prediction. For ML model creation, four algorithms were compared and tested with several data diagnosis techniques. …”
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