Pinned Repositories
8dm50-machine-learning
TU/e 8DM50 Machine Learning in Medical Imaging and Biology course materials.
AI-Projects
AI项目（强化学习、深度学习、计算机视觉、推荐系统、自然语言处理、机器导航、医学影像处理）
ai4all_nhanes
auto-sklearn
Automated Machine Learning with scikit-learn
Automated-Cardiac-Segmentation-and-Diagnosis
cancer-svm-pca-shap
With the cancer dataset, it is implemented SVM classifier and PCA. Also has SHAP to explain the model
ChatGPT
Reverse engineered ChatGPT API
chatgpt-demo
A demo repo based on OpenAI API.
Paper-Download
知网、万方、维普、爱学术文献下载（感谢anypaper）
Predicting-risk-for-Diabetes-based-in-the-urine-sample
This project uses National Health and Nutrition Examination Survey (NHANES) data to find correlation, and design a model for diabetes monitoring/detection based on the urine sample.
lixiccccc's Repositories
lixiccccc/auto-sklearn
Automated Machine Learning with scikit-learn
lixiccccc/AI-Projects
AI项目（强化学习、深度学习、计算机视觉、推荐系统、自然语言处理、机器导航、医学影像处理）
lixiccccc/ai4all_nhanes
lixiccccc/cancer-svm-pca-shap
With the cancer dataset, it is implemented SVM classifier and PCA. Also has SHAP to explain the model
lixiccccc/ChatGPT
Reverse engineered ChatGPT API
lixiccccc/chatgpt-demo
A demo repo based on OpenAI API.
lixiccccc/cs109a-final-project
Building data-driven model for predicting a person’s risk of dying based on NHANES I Epidemiologic Follow-up Study's clinical data and biochemical measurements
lixiccccc/darknet
YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
lixiccccc/dsai-wiki
:books: ApacheCN 数据科学和人工智能知识库
lixiccccc/fastai
The fastai deep learning library
lixiccccc/lcmm
:exclamation: This is a read-only mirror of the CRAN R package repository. lcmm — Extended Mixed Models Using Latent Classes and Latent Processes Report bugs for this package: https://github.com/CecileProust-Lima/lcmm/issues
lixiccccc/llama-cpp-python
Python bindings for llama.cpp
lixiccccc/Logistic-Regression-from-scratch
I coded logistic regression with gradient descent, using Framingham heart study dataset to predict whether the patient has 10-year risk of future (CHD) coronary heart disease, and do some evaluations based on error rate and McFadden's r2_score
lixiccccc/Medical-Image-Synthesis
It describes MRI to CT conversion, 3T to 7T conversion using Generative Adversarial Network (GAN).
lixiccccc/nhanes
NHANES data analysis
lixiccccc/NHANES-1
NHANES2015-2016
lixiccccc/NHANES-4
The main purpose of this project is to develop a model to predict Cardiovascular Disease and its risk factors using NHANES (National Health and Nutrition Examination Survey) dataset.
lixiccccc/ohdsi-docker
docker for ohdsi tool ecosystem
lixiccccc/PCA-with-Varimax-Rotation
lixiccccc/Predicting-Depression
Project using machine learning to predict depression using health care data from the CDC NHANES website. A companion dashboard for users to explore the data in this project was created using Streamlit. Written with python using jupyter notebook for the main project flow/analysis and visual studio code for writing custom functions and creating the dashboard.
lixiccccc/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance
Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.
lixiccccc/PubMed2PDF
A Python package to download full article PDFs from OA publications
lixiccccc/quant-learning
:books: Quant 教程整理
lixiccccc/research-method
论文写作与资料分享
lixiccccc/SES_Clustering_2020
Socioeconomic Status in Mexico
lixiccccc/shap
A game theoretic approach to explain the output of any machine learning model.
lixiccccc/sklearn-doc-zh
:book: [译] scikit-learn（sklearn） 中文文档
lixiccccc/statsmodels
Statsmodels: statistical modeling and econometrics in Python
lixiccccc/Survival-Analysis-and-Prediction
Survial analysis and prediction using various machine learning algorithms and Kaplan Meier curves
lixiccccc/Trajectory_simulation
GitHub repository for the simulation work discussed in the manuscript “Evaluation of latent-class mixed-effect models for trajectory clustering in complex data sets through simulation studies”.