An ensemble model for detection of Parkinson’s disease by comparing numerous machine learning models and XGBoost based on vocal features

Parkinson’s disease (PD), a condition of the brain, causes accidental or intractable tendencies including shaking, stiffness, and issues with balance and coordination. In most cases, symptoms start mildly and get worse with time. Patients may have problems speaking and walking as the illness worsens. Additionally, they may exhibit altered mental and behavioral patterns, sleep disorders, sadness, memory difficulty, and exhaustion. In general, it is difficult to forecast sickness. Additionally, more than 25 % of PD diagnoses are incorrect because of the significant similarity between PD symptoms and other neurological symptoms. This prompted us to conduct a comparative literature review of how cutting-edge Machine Learning (ML) implementations are used in these methodologies with their corresponding datasets, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Random Forest (RF), and Naïve Bayes (NB) classifiers. To increase accuracy, we have used multi-ensemble methods like the XGBoost Classifier and Ensemble (Majority Voting: RF & LSTM) are also used. Our results are contrasted with those from each study. The Static Spiral Test, which is used to identify tremors, performed significantly better in all experiments after applying XGBoost. As a result, it can be deduced that the multi-modal technique is efficient when used in conjunction with the ensemble method Xgboost classification (Extreme gradient boosting) and Ensemble (Majority Voting: RF & LSTM) that it offers a high accuracy of (95 %, and 96 %) in comparison to other classifier models. The approaches’ performance was assessed using a trustworthy dataset from the UCI ML repository.