Parkinson Disease Identification Research Topics

Parkinson Disease Identification Research Topics is used to identify and examine the disease at the earlier stage. In this we propose this technology and it overcomes some existing technology issues. Here we provide some concepts and details on the basis of the identification of Parkinson disease.

  1. Define automated identification of Parkinson disease?

Initially we look over the definition for Parkinson disease automated identification, this includes by employing creative technologies and computer methods to examine different data sources like movement patterns, medical imaging or video recordings to identify and examine the existence of Parkinson disease in specific.

  1. What is automated identification of Parkinson disease?

After the definition we note the deep descriptions of identification of Parkinson disease. This denotes the utilization of computer-based algorithms, technologies and models to examine and understand data across different sources, like sensor data, voice recordings or medical imaging to examine and identify the existence of Parkinson disease in specific. These automated technologies intend to offer effective and accurate detection, assisting in early examining and interference.

  1. Where automated identification used?

Next to the deep description we will discuss where to utilize this automated identification of disease. It is widely utilized in the places like Healthcare, Law enforcement, Travel and Immigration, Biometric Security Systems and Financial Transactions.

  1. Why automated identification technology proposed? , previous technology issues

Here we proposed an automated identification technology for Parkinson disease. This technology is proposed to decrease human mistakes, improve protection, and streamline procedures, manipulating the improvements like machine learning and biometrics for effective and consistent identifications in different applications. Several previous technology issues are storage, Accuracy, cost, security and real time data are overcame by proposing this research.

  1. Algorithms / Protocols

The proposed automated identification technology uses the following algorithms or methods in this research are XGBoost and LightGBM technique with Support Vector Machine (SVM), cloud computing, and Enhanced Simple Inverse Filter Tracking (ESIFT) algorithm are the methods to be utilized for this research.

  1. Comparative Study / Analysis

In this research we have to examine the methods that are compared to obtain the best outcome. Here we provide the methods to be compared are

  • EFIST method is used to improve the removal of relevant information from voice recordings affected by the Parkinson disease.
  • XGBoost and LightGBM techniques with Support Vector Machines (SVM) generate a robust categorization of system in Parkinson disease is utilized in the research of unvoiced and voiced speech.
  • Utilizing cloud computing resources is an efficient way to train and test the recommended Parkinson disease framework employing the overall dataset, addressing computational restrictions and memory.
  1. Simulation results / Parameters

The performance metrics that to be utilized for this proposed research identification of Parkinson disease. The metrics that we utilized for this research are F1-score, Accuracy, Specificity, Recall, Sensitivity and AUC-ROC are the parameters that used for our proposed research.

  1. Dataset LINKS / Important URL

Here the proposed research considers the following dataset link to incorporate it in this research. Below we provide are the dataset link for this Parkinson disease identification research.

  1. Automated identification Applications

Education, Attendance Tracking, Internet security, Automated Vehicles, Manufacturing and supply chain, smart phones and devices and Retail and E-commerce are the applications that are widely used in this research automated identification of Parkinson disease.

  1. Topology for automated identification

Now we see the topology to be used for this Parkinson disease identification research. Electrical Engineering – Critical Topology, Mathematics – General Topology, Topological Data Analysis (TDA) and Network Topology are the various topologies to be used in this research for automated identification.

  1. Environment in automated identification

The automated technology based environment that be used in this research are Operational Environment and Physical Environment these environment are employed in our Parkinson disease identification research.

  1. Simulation Tools

Parkinson disease identification is proposed in this research and it has various environments and topologies to use across this research. The tool that we employed to construct this research is Python 3.11.4. Then the research is operated by utilizing the operating system is Windows – 10 (64 – bit).

  1. Results

We propose a technology named automated identification of Parkinson disease; this overcomes several previous technology issues and is now utilized in various applications. Here the research finds the best outcomes through the comparison among various performance metrics. This research is executed by implementing the tool namely Python 3.11.4 by developing this research.

Parkinson disease Research Ideas:

Below we offered are the research topics that are relevant to the concept of Parkinson disease. These topics give assistance to us when have to clarify any doubts with the concept of automated identification:

  1. Suppression of Beta Oscillations in a Parkinson’s Disease Model by Dynamic Delayed Feedback Control
  2. A new online Arabic handwriting dataset for analyzing Parkinson’s disease
  3. Daily Monitoring of Speech Impairment for Early Parkinson’s Disease Detection
  4. A Machine Learning Model for Early Prediction of Parkinson’s Disease from Wearable Sensors
  5. In-Depth Analysis of Parkinson’s Disease: A Comprehensive Approach
  6. Screening of Mild Cognitive Impairment in Patients with Parkinson’s Disease Using a Variational Mode Decomposition Based Deep-Learning
  7. Machine Learning Methods for Predicting Parkinson’s Disease Progression
  8. Simulating The Effects of Low Intensity Focused Ultrasound in Parkinson’s Disease
  9. A Machine Learning Framework for Accurate Prediction of Parkinson’s Disease from Speech Data
  10. A Hybrid Machine Learning Framework to Improve Parkinson’s Disease Prediction Accuracy
  11. Parameter-optimized non-invasive speech test for Parkinson’s disease Severity Assessment
  12. Unique Learning Model (ULM) for Detection of Parkinson Disease Using Hand Drawings Dataset
  13. Parkinson Disease Detection using Feed Forward Neural Networks
  14. PD-Net: Multi-Stream Hybrid Healthcare System for Parkinson’s Disease Detection using Multi Learning Trick Approach
  15. Prediction of Parkinson’s Disease using Machine Learning
  16. Evaluating Orthostatic Responses with Wearable Chest-Based Photoplethysmography in Patients with Parkinson’s Disease
  17. Vocal Feature Extraction Based Hybrid ML Prototype for Parkinson’s Disease Prediction
  18. Parkinson’s disease detection from speech analysis using deep learning
  19. Smart Insole Based Shuffling Detection System for Improved Gait Analysis in Parkinson’s Disease
  20. An Enhanced Hybrid Machine Learning Model for Diagnosis of Parkinson’s Disease
  21. Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review
  22. EEG-Based Parkinson’s Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network
  23. A Quantitative Kinematic Evaluation of Postural Response in Parkinson’s Disease Subtypes
  24. BGCN: An EEG-based Graphical Classification Method for Parkinson’s Disease Diagnosis with Heuristic Functional Connectivity Speculation
  25. Generalizability of Human Activity Recognition Machine Learning Models from non-Parkinson’s to Parkinson’s Disease Patients
  26. Video-Based Quantification of Gait Impairments in Parkinson’s Disease Using Skeleton-Silhouette Fusion Convolution Network
  27. Application of Machine learning technique in the computational model of STN and GP network to predict Parkinson Disease at an early stage
  28. mm-Wave wireless radar network for early detection of Parkinson’s Disease by gait analysis
  29. Prognosis of Idiopathic Parkinson’s Disease using Convolutional Neural Networks
  30. Fuzzy KNN Implementation for Early Parkinson’s Disease Prediction
  31. Gait Assessment using Optimized Machine Learning and Feature Selection Algorithm for identifies Parkinson’s Disease
  32. Impact of Imputation Methods on Supervised Classification: A Multiclass Study on Patients with Parkinson’s Disease and Subjects with Scans Without Evidence of Dopaminergic Deficit
  33. Automatic Grading System of Parkinson’s Disease using Smart App for Remote Monitoring System
  34. Detecting Parkinson’s disease from Speech signals using Boosting Ensemble Techniques
  35. Study on Degenerative Parkinson’s Disease Using Various Machine Learning Algorithms
  36. Classification of Parkinson Disease with Feature Selection using Genetic Algorithm
  37. Machine Learning Model to Detect Parkinson’s Disease using MRI Data
  38. Budget-Based Classification of Parkinson’s Disease From Resting State EEG
  39. CNN Classification of Parkinson’s Disease using STFT Spectrum of User’s Running Speech
  40. Monitoring of Parkinson’s Disease Progression based on Speech Signal
  41. Detecting Parkinson’s Disease using Machine Learning
  42. Parkinson’s Disease Identification By Voice and Handwritten Drawings using Xgboost and Random Forest Algorithms
  43. Lax-net: Freezing of Gait Detection in Parkinson’s Disease Using LSTM with Attention and XGBoost
  44. Prediction of Freezing of Gait in Parkinson’s Disease Using Time-Series Data from Wearable Sensors
  45. Functional Connectivity Analysis Revealed Abnormal Brain Network Pattern in Parkinson’s Disease with Freezing of Gait
  46. Surface Imprinted Electroimpedance Biosensor for Detecting α-Synuclein for Parkinson’s Disease
  47. Construction of Semi-Supervised Spatial Projections to Identify the Source of Beta- and High Frequency Oscillations in Parkinson’s Disease
  48. Instrumented Canes for Parkinson’s Disease: a Survey of Commercial and Research Solutions
  49. Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space
  50. Easy Park: Mobile Application for Parkinson’s Disease Detection and Severity Level