Explainable Machine Learning in the Prediction of Depression by Christina Mimikou, Christos Kokkotis, Dimitrios Tsiptsios, Konstantinos Tsamakis, Stella Savvidou, Lillian Modig, Foteini Christidi, Antonia Kaltsatou, Triantafyllos Doskas, Christoph Mueller, Aspasia Serdari, Kostas Anagnostopoulos, Gregory Tripsianis

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Understanding Online Purchases with Explainable Machine Learning by João A. Bastos, Maria Inês Bernardes

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Explainable machine learning to predict the cost of capital by Niklas Bussmann, Paolo Giudici, Paolo Giudici, Alessandra Tanda, Alessandra Tanda, Ellen Pei-Yi Yu

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Towards Explainable Machine Learning for Prediction of Disease Progression by Stijn Berendse, Johannes Krabbe, Jonas Klaus, Faizan Ahmed

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Predicting Porosity in Freeze Casting with Explainable Machine Learning by Rafael Gaspar Bessa de Oliveira, Jones Yudi, Edson Paulo da Silva, Guillermo Alvarez Bestard, Luiz Antonio Ribeiro Junior, Alysson Martins Almeida Silva

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Explainable machine learning for predicting lung metastasis of colorectal cancer by Zhentian Guo, Zongming Zhang, Limin Liu, Yue Zhao, Zhuo Liu, Chong Zhang, Hui Qi, Jinqiu Feng, Peijie Yao

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Explainable machine learning and feature engineering applied to nanoindentation data by C.O.W. Trost, S. Žák, S. Schaffer, L. Walch, J. Zitz, T. Klünsner, H. Leitner, L. Exl, M.J. Cordill

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Recent advances in explainable Machine Learning models for wildfire prediction by Abira Sengupta, Brendon J. Woodford

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Examining peptide–gold nanoparticle interactions through explainable machine learning by Malak Gamal Abdelmeguid, Jose Isagani B. Janairo, Nishanth G. Chemmangattuvalappil

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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

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Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy by Xiangyu Shao, Wenjun Yan, Chaoying Yan, Wen Zhao, Yixuan Wang, Xia Shi, Hongchang Dong, Tianjiang Li, Junpo Yu, Peng Zuo, Zeyu Zhou, Jiming Jin

“…Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. Explainable machine learning methods can address these challenges, but they are rarely applied to rainfall threshold modeling. …”
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Detection of Alzheimer’s Disease using Explainable Machine Learning and Mathematical Models by Krishna Mahapatra, R. Selvakumar

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Enhancing automatic early arteriosclerosis prediction: an explainable machine learning evidence by Eka Miranda, Suko Adiarto

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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data by Sandeepan Dhoundiyal, Moni Shankar Dey, Shashikant Singh, Pattathal V. Arun, Guneshwar Thangjam, Alok Porwal

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Explainable machine learning framework for cataracts recognition using visual features by Xiao Wu, Lingxi Hu, Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Jiang Liu

“…Motivated by these observations, an explainable machine learning framework to recognize cataracts severity levels automatically using AS-OCT images was proposed, consisting of three stages: visual feature extraction, feature importance explanation and selection, and recognition. …”
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Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases by Mohammed Fadhil Mahdi, Arezoo Jahani, Dhafar Hamed Abd

“…In this article, a new combination of an explainable machine learning approach with a fuzzy evaluation framework is proposed to improve the diagnostic performance and interpretation of rheumatic and autoimmune diseases. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…This study aimed to develop an explainable machine learning model to predict the risk of PPD and to identify its key predictive factors.MethodsA retrospective analysis was conducted on 1,065 women who attended their 6-week postpartum follow-up visit at a tertiary maternal and child healthcare hospital in Shenyang, China, from January to December 2021. …”
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OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering

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Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach by Xiayuan Huang, Shushun Ren, Xinyue Mao, Sirui Chen, Elle Chen, Yuqi He, Yun Jiang

“…ObjectiveThis study aimed to leverage explainable machine learning models to identify and analyze the key risk factors associated with breast, colorectal, lung, and prostate cancers. …”
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