nicholast25/Depression-Diagnostic-Machine-Learning

This project focuses on analyzing and comparing the performance of SVM, Random Forest, and XGBoost in diagnosing depression based on individual data. The evaluation process incorporates Non-Parametric Statistical Testing, Feature Engineering, Resampling, and Hyperparameter Tuning. The project received a grade of 97/100 in Computational Intelligence

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younes-ammari/MedAI

Skin Disease Detection and Treatment Susceptibility by AI

Language: CSS - Size: 24.7 MB - Last synced at: 10 months ago - Pushed at: almost 2 years ago - Stars: 13 - Forks: 9

vgees/AI-for-Medical-Diagnosis

AI for Medical Diagnosis contains three assignments, 1) Chest X-Ray Medical Diagnosis with Deep Learning, 2) Evaluation of Diagnostic Models, 3) Brain Tumor Auto-Segmentation for Magnetic Resonance Imaging (MRI)

Size: 6.85 MB - Last synced at: about 1 year ago - Pushed at: almost 5 years ago - Stars: 6 - Forks: 2

philsf-biostat/philsf-biostat.github.io

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tmsalab/ohoegdm

Ordinal Higher-Order Exploratory General Diagnostic Models for Polytomous Data described by Culpepper and Balamuta (In Press) <doi:10.1080/00273171.2021.1985949>.

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philsf-biostat/Portfolio

Consultorias em Estatística Médica e Epidemiologia Clínica. CNPJ:42.154.074/0001-22

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philsf-biostat/SAR-2021-012-JG

Quantificação do efeito da receita recebida na autodenominação como evangélicos em deputados federais de 2018

Language: R - Size: 2.03 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0