Detection Of Parkinsons Disease Using Python

By MD ANAS

The "Detection of Parkinson's Disease using Python" project aims to develop a machine learning model that can accurately diagnose Parkinson's disease based on various physiological features.

The "Diagnosing Parkinson's Disease Using Python" project aims to create a learning machine that can accurately diagnose Parkinson's disease based on various physiological characteristics. Here is a brief description of how the project works: Data Collection: Collection of data containing information about people, including healthy people and Parkinson's disease subjects. The data should include features such as audio recordings, demographic information, medical history, and various measurements. Data Preprocessing: Cleans and preprocesses datasets by processing missing values, removing outliers, and performing feature scaling or standardization as needed. For model evaluation, split the data into training and testing subsets.Feature Selection/Engineering: Identifying key features associated with Parkinson's disease diagnosis. This may include statistical analysis, correlation analysis or domain information to select the most informative data for the model. Model Selection: Select the appropriate machine learning model for the classification task. Commonly used algorithms for Parkinson's disease detection include support vector machines (SVM), decision trees, random forests, or neural networks. Model Training and Validation: Train the selected model using the training data and evaluate its performance using appropriate metrics such as accuracy, precision, recall, or area behind the receiver operating characteristic (ROC) curve.Adjust model parameters and methods such as competition to optimize the model's performance. Evaluation and Evaluation Performance: Evaluate the learned model using test data to assess its ability to identify healthy individuals or detect the presence of a Parkinson's disease. Analyzes the performance evaluation model and evaluates its effectiveness in disease diagnosis. Delivery and future development: When the performance of the model is satisfactory, it can be used to predict Parkinson's disease based on new, unseen data. Further development may involve fine-tuning the model, including additional data or features, or exploring other advanced machine learning methods to improve model accuracy.