Unique Learning Model (ULM) for Detection of Parkinson Disease Using Hand Drawings Dataset

Parkinson's disease (PD) is a brain disorder that affects a person's daily life, causing tremors, stiffness, and difficulty with balance and coordination. Machine learning (ML) can be used to detect Parkinson's disease by analyzing patterns in data, such as hand drawings. In this paper, the unique learning model (ULM) used for hand drawings to detect PD; a dataset would need to be collected containing drawings from both people with Parkinson's disease and healthy individuals. The dataset should be large enough to ensure that the machine learning algorithm can accurately distinguish between the two groups. Once the dataset is collected, a machine learning algorithm can be trained to classify the drawings as either Parkinson's or healthy. The machine learning algorithm would need to be trained on the dataset using features extracted from the hand drawings, such as the size and shape of certain parts of the drawing, the angle of the lines, and the smoothness of the lines. These features would be used to create a model that can accurately distinguish between Parkinson's and healthy hand drawings. The performance of the model analyzed by a separate testing dataset would be used, containing hand drawings from both groups. The model would be evaluated on this dataset to determine its accuracy, precision, recall, and F1 score. These metrics can be used to assess how well the model is able to distinguish between Parkinson's and healthy hand drawings. Overall, using ULM to detect PD from dataset has the potential to be a non-invasive, low-cost, and accessible method for early detection of the disease.