An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 1 | march 2023
Published
09 May, 2023
Parkinson Disorder (PD) is a neurological disorder which is by nature progressive and degenerative. Dysphonia, a voice-based disorder is the most vital symptom exhibited by the 90% PD patients. PD has no cure and has no unique test. The delay in progression of PD can be made by the early diagnosis of the disease. The early diagnosis system can be made more accurate and effective by the incorporation of Artificial Intelligence (AI) technique. AI has a widespread application ranging from enterprise systems to small scale system. The proposed system aims to develop an AI based early diagnosis system based on voice features modality. The proposed system presents a Homogenous Decision Tree Regressor Ensemble model which predicts the Unified Parkinson Disorder Rating Score based on voice features. The proposed model is compared with the existing Decision Tree Regressor model. The suggested model is developed and tested with 42 PD patients voice features dataset. The evaluation metrics used are Mean Absolute Error, Mean Squared Error, and Co-efficient of Determination (R-Squared). It is evident from the results that the proposed model produces less error compared to the existing model.
KeywordsParkinson Disorder (PD) Artificial Intelligence (AI) Homogenous Regressor Models
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