Machine Learning System for Parkinson's Disease Detection

[nidhipandey73]

hello @yashasvini121, Problem Description Parkinson's Disease is a progressive neurological disorder that affects movement, often causing tremors, stiffness, and difficulty with balance and coordination. Early detection is crucial for improving treatment outcomes and managing the progression of the disease. Traditional diagnostic methods are often invasive, expensive, or time-consuming. The aim of this project is to develop a machine learning model that can accurately detect Parkinson's Disease using accessible data, such as vocal or motor control features, in a non-invasive manner. The solution will help in quicker and cost-effective early diagnosis, improving patient care and treatment management.

Model Description The machine learning model will use a Support Vector Machine (SVM), a robust supervised learning algorithm particularly well-suited for binary classification tasks like disease detection. SVM works by finding the optimal hyperplane that separates the data into different classes (in this case, Parkinson's vs. non-Parkinson's). The dataset will consist of features such as vocal measurements or motor control indicators. Preprocessing steps such as feature scaling, data cleaning, and splitting into training and test sets will be applied. The model will undergo cross-validation and hyperparameter tuning to improve accuracy. SVM is appropriate for this task because it can handle high-dimensional data efficiently and is known for its effectiveness in medical diagnostics.

Estimated Time for Completion The estimated time to fully implement and test the model is approximately 1 week.

Expected Outcome The expected outcome is a reliable, high-accuracy SVM model capable of detecting Parkinson's Disease with significant predictive power. Metrics such as accuracy, precision, recall, and ROC-AUC will demonstrate the model's effectiveness. The model will offer a non-invasive, rapid diagnostic tool for early detection of Parkinson's Disease, which can be scaled for use in medical practices or research settings. Ideally, the system will contribute to faster diagnoses, improving patient care and treatment management.

Additional Context This model will be based on publicly available datasets such as the UCI Parkinson's dataset, which includes biomedical voice measurements. Preprocessing will involve removing outliers and handling missing values. Libraries like scikit-learn, pandas, and NumPy will play a key role in implementing the model. Visualizations of data and model performance will be generated using matplotlib and seaborn.

[nidhipandey73]

Kindly assign this to me and add hacktoberfest and gssoc-extdrelated tags. Thank you.

[yashasvini121]

Sure, @nidhipandey73, go ahead!. A model for this problem statement has already been added using XGBClassifier. Therefore, please create a new folder with a name that ends with _svm or something else.

[nidhipandey73]

Thank you so much for this opportunity.

On Fri, Oct 4, 2024, 9:22 PM Yashasvini Sharma @.***> wrote:

Assigned #36 https://github.com/yashasvini121/predictive-calc/issues/36 to @nidhipandey73 https://github.com/nidhipandey73.

— Reply to this email directly, view it on GitHub https://github.com/yashasvini121/predictive-calc/issues/36#event-14521259162, or unsubscribe https://github.com/notifications/unsubscribe-auth/A72PA3BRMPUM2BLQBJBGAXDZZ22VXAVCNFSM6AAAAABPKHQTFKVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJUGUZDCMRVHEYTMMQ . You are receiving this because you were assigned.Message ID: @.*** com>

[akanil18]

Please assign this task to me and include the tags Hacktoberfest

[yashasvini121]

@akanil18, the issue is already assigned, but feel free to create a new PR explaining your unique approach. We'll review how it complements the existing work.