Deep learning-based differential gut flora for prediction of Parkinson's

Background: There had been extensive research on the role of the gut microbiota in human health and disease. Increasing evidence suggested that the gut-brain axis played a crucial role in Parkinson's disease, with changes in the gut microbiota speculated to be involved in the pathogenesis of Parkinson's disease or interfere with its treatment. However, studies utilizing deep learning methods to predict Parkinson's disease through the gut microbiota were still limited. Therefore, the goal of this study was to develop an efficient and accurate prediction method based on deep learning by thoroughly analyzing gut microbiota data to achieve the diagnosis of Parkinson's disease.

Methods: This study proposed a method for predicting Parkinson's disease using differential gut microbiota, named the Parkinson Gut Prediction Method (PGPM). Initially, differential gut microbiota data were extracted from 39 Parkinson's disease (PD) patients and their corresponding 39 healthy spouses. Subsequently, a preprocessing method called CRFS (combined ranking using random forest scores and principal component analysis contributions) was introduced for feature selection. Following this, the proposed LSIM (LSTM-penultimate to SVM Input Method) approach was utilized for classifying Parkinson's patients. Finally, a soft voting mechanism was employed to predict Parkinson's disease patients.

Results: The research results demonstrated that the Parkinson gut prediction method (PGPM), which utilized differential gut microbiota, performed excellently. The method achieved a mean accuracy (ACC) of 0.85, an area under the curve (AUC) of 0.92, and a receiver operating characteristic (ROC) score of 0.92.

Conclusion: In summary, this method demonstrated excellent performance in predicting Parkinson's disease, allowing for more accurate predictions of Parkinson's disease.