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Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
Li, Jian Ping, Haq, Amin Ul, Din, Salah Ud, Khan, Jalaluddin, Khan, Asif, Saboor, Abdus
Published in IEEE access (2020)
Published in IEEE access (2020)
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Machine-Learning Classification of Pulse Waveform Quality
Ouyoung, Te, Weng, Wan-Ling, Hu, Ting-Yu, Lee, Chia-Chien, Wu, Li-Wei, Hsiu, Hsin
Published in Sensors (Basel, Switzerland) (01.11.2022)
Published in Sensors (Basel, Switzerland) (01.11.2022)
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TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets
Satu, Md. Shahriare, Khan, Md. Imran, Mahmud, Mufti, Uddin, Shahadat, Summers, Matthew A., Quinn, Julian M.W., Moni, Mohammad Ali
Published in Knowledge-based systems (17.08.2021)
Published in Knowledge-based systems (17.08.2021)
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Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data
Dyrba, Martin, Barkhof, Frederik, Fellgiebel, Andreas, Filippi, Massimo, Hausner, Lucrezia, Hauenstein, Karlheinz, Kirste, Thomas, Teipel, Stefan J
Published in Journal of neuroimaging (01.09.2015)
Published in Journal of neuroimaging (01.09.2015)
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Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis
Yassin, Walid, Nakatani, Hironori, Zhu, Yinghan, Kojima, Masaki, Owada, Keiho, Kuwabara, Hitoshi, Gonoi, Wataru, Aoki, Yuta, Takao, Hidemasa, Natsubori, Tatsunobu, Iwashiro, Norichika, Kasai, Kiyoto, Kano, Yukiko, Abe, Osamu, Yamasue, Hidenori, Koike, Shinsuke
Published in Translational psychiatry (17.08.2020)
Published in Translational psychiatry (17.08.2020)
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Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy
Vergara, Victor M., Mayer, Andrew R., Damaraju, Eswar, Kiehl, Kent A., Calhoun, Vince
Published in Journal of neurotrauma (01.03.2017)
Published in Journal of neurotrauma (01.03.2017)
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Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity
Posada–Quintero, Hugo F., Kong, Youngsun, Chon, Ki H.
Published in American journal of physiology. Regulatory, integrative and comparative physiology (01.08.2021)
Published in American journal of physiology. Regulatory, integrative and comparative physiology (01.08.2021)
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Machine learning classification of ADHD and HC by multimodal serotonergic data
Kautzky, A., Vanicek, T., Philippe, C., Kranz, G. S., Wadsak, W., Mitterhauser, M., Hartmann, A., Hahn, A., Hacker, M., Rujescu, D., Kasper, S., Lanzenberger, R.
Published in Translational psychiatry (07.04.2020)
Published in Translational psychiatry (07.04.2020)
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Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels
Moctezuma, Luis Alfredo, Molinas, Marta, Abe, Takashi
Published in BioMed research international (01.01.2025)
Published in BioMed research international (01.01.2025)
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Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative
Ashinsky, Beth G., Bouhrara, Mustapha, Coletta, Christopher E., Lehallier, Benoit, Urish, Kenneth L., Lin, Ping‐Chang, Goldberg, Ilya G., Spencer, Richard G.
Published in Journal of orthopaedic research (01.10.2017)
Published in Journal of orthopaedic research (01.10.2017)
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Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification
Zhou, Yong, Tan, Yizhou, Wang, Shasha, Cai, Hanshu, Gu, Ying
Published in Biomedical engineering online (12.08.2025)
Published in Biomedical engineering online (12.08.2025)
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Assessing neural markers of attention during exposure to construction noise using machine learning classification of electroencephalogram data
Mir, Mostafa, Nasirzadeh, Farnad, Zakeri, Morteza, Hill, Aron, Karmakar, Chandan
Published in Building and environment (01.08.2024)
Published in Building and environment (01.08.2024)
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A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals
Bin Heyat, Md Belal, Akhtar, Faijan, Khan, Asif, Noor, Alam, Benjdira, Bilel, Qamar, Yumna, Abbas, Syed Jafar, Lai, Dakun
Published in Applied sciences (01.11.2020)
Published in Applied sciences (01.11.2020)
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