https://github.com/mkk-1817/adhd-prediction
This project focuses on leveraging machine learning techniques to predict Attention-Deficit/Hyperactivity Disorder (ADHD) in children. Accurate and early diagnosis is crucial for effective intervention and support.
https://github.com/mkk-1817/adhd-prediction
adhd data-analysis data-science jupyter-notebook machine-learning machine-learning-algorithms prediction python
Last synced: 3 months ago
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This project focuses on leveraging machine learning techniques to predict Attention-Deficit/Hyperactivity Disorder (ADHD) in children. Accurate and early diagnosis is crucial for effective intervention and support.
- Host: GitHub
- URL: https://github.com/mkk-1817/adhd-prediction
- Owner: mkk-1817
- Created: 2023-12-21T14:29:29.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-23T08:36:47.000Z (about 2 years ago)
- Last Synced: 2025-02-21T19:19:01.376Z (11 months ago)
- Topics: adhd, data-analysis, data-science, jupyter-notebook, machine-learning, machine-learning-algorithms, prediction, python
- Language: Jupyter Notebook
- Homepage:
- Size: 26.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# ADHD-Prediction
## Dataset
The dataset includes 121 children aged 7-12, with 61 diagnosed with ADHD based on DSM-IV criteria and 60 healthy controls without psychiatric disorders, epilepsy, or high-risk behaviors.
## Methodology
Key machine learning concepts like data preprocessing, feature engineering, and classification algorithms are employed. Data preprocessing involves handling missing values, normalizing features, and encoding categorical variables. Feature engineering extracts relevant information from demographic details, clinical history, and medication usage.
## Machine Learning Algorithms
Various algorithms such as logistic regression, naive bayes estimator,Artificial Neural Networks(ANN), KMeans Clustering, Decision Tree for Level CLassification are evaluated to identify the most effective model for ADHD prediction.
## Performance Metrics
Model performance is evaluated using metrics like accuracy, precision, recall, and F1-score to measure the model's ability to differentiate between children with ADHD and healthy controls.