Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia by Hao Chen, Shurui Zhang, Hiromi Matsumoto, Nanami Tsuchiya, Chihiro Yamada, Shunsuke Okasaki, Atsushi Miyasaka, Kentaro Yumoto, Daiki Kanou, Fumihiro Kashizaki, Harumi Koizumi, Kenichi Takahashi, Masato Shimizu, Nobuyuki Horita, Takeshi Kaneko

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Advanced R-GAN: Generating anomaly data for improved detection in imbalanced datasets using regularized generative adversarial networks by Junhak Lee, Dayeon Jung, Jihoon Moon, Seungmin Rho

“…To ensure fairness and validate the effectiveness of our data generation process, we used PyCaret's automated machine learning framework to rigorously test different machine learning models, ultimately identifying the light gradient boosting machine as the most effective. …”
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Tim-1-mediated extracellular matrix promotes the development of hepatocellular carcinoma by Hua Ruheng, Yu Pengfei, Zheng Wanting, Wu Nuwa, Yu Wangjianfei, Kong Qingyu, He Jun, Qin Lei

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Fruit size prediction of tomato cultivars using machine learning algorithms by Masaaki Takahashi, Yasushi Kawasaki, Hiroki Naito, Hiroki Naito, Unseok Lee, Koichi Yoshi

“…We aimed to develop a method for early prediction of tomato fruit size at harvest with machine learning algorithm, and three machine learning models (Ridge Regression, Extra Tree Regrreion, CatBoost Regression) were compared using the PyCaret package for Python. For constructing the models, the fruit weight estimated from the fruit diameter obtained over time for each cumulative temperature after anthesis was used as explanatory variable and the fruit weight at harvest was used as objective variable. …”
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A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa by Maria Magdalena Van Zyl-Cillié, Jacoba H. Bührmann, Alwiena J. Blignaut, Derya Demirtas, Siedine K. Coetzee

“…In this study, supervised machine learning models were developed to identify the factors that most strongly predict nurses reporting feelings of burnout and experiencing emotional exhaustion. Methods The PyCaret 3.3 package was used to develop classification machine learning models on 1165 collected survey responses from nurses across South Africa in medical-surgical units. …”
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Collagen prolyl 4-hydroxylase subunit α member-induced head and neck squamous cell carcinoma aggressiveness is antagonized by LLGL2 via redu... by Xu Miao, Shi Run, Yang Jie, Chen Heng, Liu Shihua, Yu Shupei, Li Sasa, He Wenqiang, Sy Man-Sun, Lu Mingjian, Zhang Huixia, Li Chaoyang

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Vaccarin suppresses diabetic nephropathy through <?A3B2 pi6?>inhibiting the EGFR/ERK1/2 signaling pathway by Zhu Xuexue, Meng Xinyu, Du Xinyao, Zhao Chenyang, Ma Xinyu, Wen Yuanyuan, Zhang Shijie, Hou Bao, Cai Weiwei, Du Bin, Han Zhijun, Xu Fei, Qiu Liying, Sun Haijian

“…> <?Pub Caret 40?>…”
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