Enhancing Early Detection of Parkinson's Disease with Machine Learning: Insights from Xception Architecture and Random Forest Classifier

The degenerative neurological disorder known as Parkinson's disease impacts millions of people around the globe. Lack of dopamine, a substance that helps brain neurons communicate, causes the onset of Parkinson's disease. Symptoms of movement dysfunction in Parkinson's disease include when 60-80% of these cells die, preventing dopamine production. Parkinson's disease treatment requires early and accurate diagnosis. Machine learning algorithms can identify clinical data linkages and patterns doctors may overlook. Early identification and treatment of Parkinson's disease can benefit from machine learning for sickness prediction. This research uses xception architecture and the Random Forest Classifier to predict Parkinson's onset. This experiment used UCI ML repository electronic medical records for clinical data. The model had 100% Train Accuracy and 97% Test Accuracy. These findings suggest machine learning can diagnose and forecast Parkinson's disease. This project shows findings on Parkinson's disease diagnosis. Python libraries sci-kit-learn/tensor flow, NumPy, pandas, matplotlib, Keras, etc. will improve our model's accuracy. Using exception architecture and a Random Forest Classifier, we will build a model.