IRJET- Prediction of Parkinson's Disease using Data Mining: A Survey (original) (raw)

A Prototype of Parkinson’s and Primary Tumor Diseases Prediction Using Data Mining Techniques

In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the grade of disease with affirmation. Generally many tests are done that involve classification or clustering of large scale data. However , many tests can complicate the main diagnosis process and lead to the difficulty in obtaining end results, particularly in the case where many tests are performed. This could be solved by the aid of machine learning techniques. In this paper Psychiatric datasets of Parkinson’s & Primary tumor Diseases are modeled and used it to predict their probability in the patients. The performance of Artificial Neural Network , Decision Trees Algorithm and Naive Bayes Algorithm on these medical data were measured. The results showed that Artificial Neural Network performed best with accuracy of 90.7692 % ,then Decision trees with accuracy of 80.5128 % and finally NaiveBayes with accuracy 69.2308 % in case of Parkinson’s while as in primary tumor NavieBayes performs best with an accuracy of 49.1176%, then Artificial Neural Network with an accuracy of 42.0588% and lastly Decision trees with accuracy 32.3529%.

Parkinson’s Disease Prediction System in Machine Learning

ITM Web of Conferences

Around the globe, thousands of people worldwide are suffering by Parkinson’s Disease (PD), a central nervous system degenerative condition. Early detection and diagnosis of PD is crucial for successful treatment and management of the disease. In past few years, Machine learning (ML) algorithms has shown great potential in predicting PD based on various physiological and neurological markers. In this disease prediction system, a system is proposed using ML-based approach to predict the presence of PD in patients. The system employs various machine learning models, including Gradient Boosted Tree, random forest, and logistic regression, to identify key markers and patterns associated with the disease. Overall, this disease prediction system provides a valuable tool for early detection and diagnosis of PD, which can lead to better management and treatment of the disease. The proposed approach can also be extended to other neurological disorders, providing a general framework for diseas...

Data-Driven Based Approach to Aid Parkinson’s Disease Diagnosis

Sensors

This article presents a machine learning methodology for diagnosing Parkinson’s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This ...

An improved approach for prediction of Parkinson's disease using machine learning techniques

2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from Parkinson's disease worldwide is around 5 millions. Thus, it is important to predict Parkinson's disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson's disease, however an increasing amount of research is being done to predict the Parkinson's disease from non-motor symptoms that precede the motor ones. If early and reliable prediction is possible then a patient can get a proper treatment at the right time. Nonmotor symptoms considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. Developing machine learning models that can help us in predicting the disease can play a vital role in early prediction. In this paper we extend a work which used the non-motor features such as RBD and olfactory loss. Along with this the extended work also uses important biomarkers. In this paper we try to model this classifier using different machine learning models that have not been used before. We developed automated diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and Boosted Logistic Regression. It has been observed that Boosted Logistic Regression provides the best performance with an impressive accuracy of 97.159 % and the area under the ROC curve was 98.9%. Thus, it is concluded that this models can be used for early prediction of Parkinson's disease.

IJERT-Prediction of Parkinson's Disease using Machine Learning Techniques

International Journal of Engineering Research and Technology (IJERT), 2019

https://www.ijert.org/prediction-of-parkinson https://www.ijert.org/research/prediction-of-parkinsons-disease-using-machine-learning-techniques-IJERTCONV7IS01030.pdf Data mining is a process of discovering useful knowledge from database to build a structure (i.e., model or pattern) that can meaningfully interpret the data. It has been defined as a process of discovering interesting patterns and knowledge from large amount of data. It uses the machine learning techniques to discover hidden pattern in the data. These techniques can be in the three main categories which are supervised learning techniques, unsupervised learning techniques and semi-supervised learning techniques. Expert systems developed by machine learning techniques can be used to assist physicians in diagnosing and predicting diseases. Due to diseases diagnosis importance to mankind, several studies have been conducted on developing methods for their classification. Although these techniques can be used to predict the PD through a set of real-world datasets, however the most methods developed by supervised prediction techniques in the previous researches do not support the incremental updates of the data for PD prediction. K-mean clustering, standard supervised techniques cannot be used for the incremental learning and therefore they require recomputing all the training data to build prediction models. The method proposed in this study has been evaluated by a public datasets from UCI which have input and output parameters for PD diagnosis. In addition, compared to the bighealthcare data, the nature of the data in these datasets is not complex. In addition, in case of big healthcare data which can be complex datasets with unique characteristics, the future studies need to consider this issue in the development of new method sin order to overcome the challenges of data processing time and take advantage of big data. Bayesian classification, as big healthcare data include multi-spectral, heterogeneous, imprecise and incomplete observations (e.g., diagnosis) which are derived from different sources, therefore new methods are needed and relying solely on conventional machine learning techniques may include some shortcomings in predicting the disease.

PERFORMANCE OF CLASSIFICATION TECHNIQUES ON PARKINSON'S DISEASE

International Journal of Advances in Science Engineering and Technology, 2017

Nowadays, many methods and algorithms have been developed that may influence the decision-making process and are used to extract meaningful information. One of the well know methods or approaches in information extraction is data mining. Data mining tries to establish the best model to support decision system, to extract information and to categorize, to summarize and etc. according to given data set. The Parkinson's disease-related data obtained from UCI Machine Learning Database is used to try several data mining techniques and methods to see the successes of techniques regarding to diagnosis accuracy ratio to support the expert. So far, Parkinson's disease can actually be diagnosed after medical examinations. However, diagnosis with computer has been the subject of many researches due to demand to help physician. In this study, a research is conducted using 16 different data mining techniques and methods to support the doctors in the decision-making process. The results of the applied methods for the study regarding to diagnosis accuracy ratesare as follows; IB1 (96.4103%), RotationForest (92.3077%) RandomForest (91.7949%), MultilayerPerceptron (90.7692%),

Parkinson's Disease Prediction Using Machine Learning Models

International Journal of Advances in Computer Science and Technology , 2024

Parkinson's disease is a neurodegenerative condition that affects billions of persons worldwide. This abstract aims to shed light on the causes and consequences of this debilitating condition. The primary cause of Parkinson's disease is the progressive degeneration of dopaminergic neurons in the substantia nigra region of brain. This neuronal loss results in a depletion of dopamine, a crucial neurotransmitter responsible for regulating movement and coordination. Therefore, individuals with Parkinson's disease have symptoms like tremors, rigidity, bradykinesia, and postural instability. These signs profoundly impact the quality of life, causing difficulties with daily activities and reducing independence. In addition to motor symptoms, non-motor symptoms such as depression, cognitive impairment, and autonomic dysfunction often accompany the disease, further complicating the clinical picture. Research into the causes and consequences of Parkinson's disease is ongoing, with a focus on using efficient medications and refining the quality of life for those affected by this condition. Now by Using machine learning algorithms, we can predict whether a person has a specific disease based on input values like gender and age. These algorithms analyze patterns and relationships in data to get predictions about an individual's health status. This technology can assist in early disease detection and improve healthcare outcomes..

A Decision Support System For Parkinsons Disease Diagnosis Using Classification And Regression Tree

Journal of Mathematics and Computer Science, 2012

Parkinson's disease (PD) is a progressive disorder of the nervous system that affects movement. It develops gradually, often starting with a barely noticeable tremor in just one hand. But while tremor may be the most well-known sign of Parkinson's disease, the disorder also commonly causes a slowing or freezing of movement. Parkinson's disease is the second most common Neurodegenerative action only surpassed by Alzheimer's disease. However, a proper diagnosis at an early stage can result in significant life saving. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. The present study compares the accuracy of several machine learning methods including Bayesian Networks, Regression, Classification and Regression Trees (CART), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) for proposing a decision support system for diagnosis of parkinson's disease. The proposed system achieved an accuracy of 93.7% using classification and regression tree. Sensitivity analysis via classification and regression tree was also used to find importance of input variables.

A Comparative Analysis for Prediction of Parkinson’s Diseases using Classification Algorithm

International Journal for Research in Applied Science and Engineering Technology, 2020

Machine learning has secured a significant name in health care sector because of its ability of improving the accuracy and time for disease prediction. Recently, Parkinson's is a noteworthy chronic diseases worldwide. It is observed that more than one million cases are common in India .There is the chances that 1.2 million people will be suffering with this diseases in the US at the end of 2030. Thus, early stage prediction of people's parkinson's severity is important in order to make fast planning of necessary treatment. In our study, we proposed a framework which will be helpful in real time prediction of parkinsons. We have use UCI Machine learning repository dataset which contains the acoustic features of voice recordings. Dataset is divided into 8:2 ratio as train and test data respectively. We use four important machine learning classfication technique i.e. SVM, Logistic regression, Extra tree classifier, Decision tree classifier for predicting parkinsons. 80% of the dataset is used to train the model for specified classification technique and validation is done over rest 20% of data. The performance measurement of the proposed classification technique was evaluated by applying the 10-Fold cross validation technique. The result shows that svm provides highest performance with tha accuracy 81%.

A Comparative Study on Remote Tracking of Parkinson’s Disease Progression Using Data Mining Methods

International Journal in Foundations of Computer Science & Technology, 2013

In recent years, applications of data mining methods are become more popular in many fields of medical diagnosis and evaluations. The data mining methods are appropriate tools for discovering and extracting of available knowledge in medical databases. In this study, we divided 11 data mining algorithms into five groups which are applied to a dataset of patient's clinical variables data with Parkinson's Disease (PD) to study the disease progression. The dataset includes 22 properties of 42 people that all of our algorithms are applied to this dataset. The Decision Table with 0.9985 correlation coefficients has the best accuracy and Decision Stump with 0.7919 correlation coefficients has the lowest accuracy.