https://github.com/simhayn/binary-classification
Alzheimer's disease detection using XGBoost and other prediction models.
https://github.com/simhayn/binary-classification
alzheimer-disease-prediction binary-classification exploratory-data-analysis mental-health prediction-model python scikit-learn xgboost
Last synced: about 2 months ago
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Alzheimer's disease detection using XGBoost and other prediction models.
- Host: GitHub
- URL: https://github.com/simhayn/binary-classification
- Owner: simhayn
- Created: 2024-06-24T11:19:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-04T20:58:20.000Z (over 1 year ago)
- Last Synced: 2024-12-27T05:06:48.863Z (12 months ago)
- Topics: alzheimer-disease-prediction, binary-classification, exploratory-data-analysis, mental-health, prediction-model, python, scikit-learn, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 5.39 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
README
## Doctor, is he sick? does he have Alzheimer's disease? 😯🩺
Let's see..
well, this algorithm can detect if the patient is sick with 96.6% accuracy
The most **relevant features** for this **xgboost** model are as follows:
1. **Functional Assessment** (ranging from 0 to 10. Lower scores indicate greater impairment)
2. **ADL (Activities of Daily Living** score, ranging from 0 to 10. Lower scores indicate greater impairment)
3. **MMSE (Mini-Mental State Examination** score, ranging from 0 to 30. Lower scores indicate cognitive impairment)
4. **Memory Complaints**
5. **Behavioral Problems**
And there are some more (not so much contributing) features:
6. Cholesterol HDL
7. Diet Quality
8. Sleep Quality
9. Alcohol Consumption
10. Cholesterol Triglycerides
the model was fit to patients who had normal levels of cholesterol and blood pressure, in the ages of 60-90 years old.
## Binary Classification- Alzheimers
In this project I explored the 🧠Alzheimers disease dataset downloaded from Kaggle.com
Main steps:
- **Exploratory Data Analysis** (EDA): Visualize features' relationships and distributions. Check some statistics.
- **Data Preprocessing**: Scale the cumulative features. Covert categorical features to binary.
- **Model Training**: Train some models on the preprocessed data *(Models: SVM, Logistic Regression, Random Forest, GBoost, Naive Bayes, XGBoost with Random searchCV, Neural Networks).* Select 10 features with higher importance to improve the models.
- **Model Evaluation**: Evaluate the models' performances. Plot the Neural Networks' validation graph.
Hope you enjoy this project!
There are more projects of binary classification for healthcare datasets in this repo. feel free to check them out!