Alzheimer's Detection

[ananyag309]

🚀 Alzheimer's Detection Model Proposal

🔍 Problem Description:

The need for an efficient and accurate method to detect Alzheimer's disease in its early stages is crucial. Early diagnosis can significantly improve patient outcomes, but traditional methods can be time-consuming, costly, and may not always catch subtle cognitive decline. This project aims to develop a machine learning-based approach for early Alzheimer's detection using clinical and demographic data.

🧠 Model Description:

The proposed solution involves building a machine learning model that predicts the likelihood of Alzheimer's disease based on patient data. The model may utilize algorithms such as Random Forest, Support Vector Machines (SVM), or neural networks. Simpler models like logistic regression or k-nearest neighbors will also be considered for comparison. Key tasks will include data preprocessing, model training, and hyperparameter tuning to achieve optimal accuracy.

⏲️ Estimated Time for Completion:

• Data Collection and Preprocessing: 1-2 weeks
• Model Training and Evaluation: 2-3 weeks
• Feature Analysis and Hyperparameter Tuning: 1-2 weeks
• Final Testing and Integration: 1 week

🎯 Expected Outcome:

The model should provide reliable predictions regarding Alzheimer's risk, highlighting key features associated with the disease. The solution will be user-friendly and easy to integrate, offering insights to support early diagnosis and benefiting the medical community and machine learning researchers.

📄 Additional Context:

• The project will follow best practices in machine learning, including data cleaning, cross-validation, and model evaluation using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
• The repository should include a predict.py script or a notebook with a model_details() function to generate a detailed model report.

To be Mentioned while taking the issue:

• Specify your participant role : GSSOC-ext