When I was Searching About Parkinson's disease, I got to observe an Journal paper Titled "An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease" Which was Published by Jiahui Zhang, You Li, Yuyuan Gao, Jinlong Hu, Biao Huang, Siming Rong, Jianing Chen, Yuhu Zhang, Limin Wang, Shujun Feng, Lijuan Wang, Kun Nie.
Original Paper link: Parkinson's Disease
Then I Got an Idea to Write a code by reading journal and Make it simple.
Developing a machine learning model for identifying PD-MCI using SBM is a promising approach. Consider collecting a well-curated dataset with relevant features, such as surface-based morphometric data. Employ a suitable algorithm like SVM or deep learning architectures for classification. Regular validation and collaboration with healthcare professionals can enhance the model's accuracy and reliability.
Here, The code generates a synthetic dataset with SBM-like features and binary labels for PD-MCI, then trains a linear Support Vector Machine (SVM) model to predict PD-MCI based on the features.
But, Got An Accuracy of 45.50%