scholarly journals A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
João W. M. de Souza ◽  
Shara S. A. Alves ◽  
Elizângela de S. Rebouças ◽  
Jefferson S. Almeida ◽  
Pedro P. Rebouças Filho
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Similarity Analysis ◽  
New Approach ◽  
Promising Tool ◽  
The World ◽  
Better Than

Parkinson’s disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson’s disease.

Development of a Stacking-Based Ensemble Machine Learning for Detection of Depression in Parkinson’s Disease: Preliminary Research

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Haewon Byeon
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Depression Scale ◽  
Prognostic Index ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Outcome Variable

This preliminary study used the stacking ensemble to explore the major elements (factors) which could predict depression in patients with Parkinson’s disease and presented baseline data for developing a nomogram prognostic index for predicting high-risk groups for depression among patients with Parkinson’s disease in the future. Depression, an outcome variable, was divided into “with depression” and “without depression” using the Geriatric Depression Scale-30 (GDS-30). This study developed nine machine learning models (ANN, random forest, naive bayes, CART, ANN+LR, random forest+LR, naive bayes+LR, CART+LR, and random forest+naive bayes+CART+ANN+LR). The predictive performance (e.g., REMS, IA, Ev) of each machine learning model was validated through 10-fold cross-validation. The analysis results showed that the random forest+LR had the best predictive performance: RMSE = 0.16, IA = 0.73, and Ev = 0.48. This study analyzed the normalized importance of the random forest+LR model’s variables (the final model) and confirmed that K-MMSE, K-MoCA, Global CDR, sum of boxes in CDR, total score of UPDRS, motor score of UPDRS, K-IADL, H and Y staging, Schwab and England ADL, and REM and RBD were ten major variables with high weight among predictors of Parkinson’s disease with depression in South Korea. It is necessary as well to develop interpretable machine learning to build a model for predicting depression in patients with Parkinson’s disease that can be used in the medical field.

scholarly journals Computer keyboard interaction as an indicator of early Parkinson’s disease

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
L. Giancardo ◽  
A. Sánchez-Ferro ◽  
T. Arroyo-Gallego ◽  
I. Butterworth ◽  
C. S. Mendoza ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
New Approach ◽  
Early Stages ◽  
Current Medical Practice ◽  
Undue Burden ◽  
Computer Keyboard ◽  
Time Required ◽  
Early Parkinson’S Disease ◽  
Better Than

Abstract Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).

scholarly journals Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

2020 ◽  
Vol 16 (1) ◽  
pp. 155014771989521
Author(s):  
Sujata Dash ◽  
Ajith Abraham ◽  
Ashish Kr Luhach ◽  
Jolanta Mizera-Pietraszko ◽  
Joel JPC Rodrigues
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Firefly Algorithm ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Naïve Bayes ◽  
Stochastic Algorithm

Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chaotic maps which will increase the diversification and intensification capability of chaos-based firefly algorithm. The objective of chaos-based maps is to select initial values of the population of fireflies and change the value of absorption coefficient so as to increase the diversity of populations and improve the search process to achieve global optima avoiding the local optima. For selecting the most discriminant features from the search space, Naïve Bayesian stochastic algorithm with kernel density estimation as learning algorithm is applied to evaluate the discriminative features from different perspectives, namely, subset size, accuracy, stability, and generalization. The experimental study of the problem established that chaos-based logistic model overshadowed other chaotic models. In addition, four widely used classifiers such as Naïve Bayes classifier, k-nearest neighbor, decision tree, and radial basis function classifier are used to prove the generalization and stability of the logistic chaotic model. As a result, the model identified as the best one and could be used as a decision making tool by clinicians to diagnose Parkinson’s disease patients.

scholarly journals Diagnostic potential of gut microbiota in Parkinson’s disease

2020 ◽  
Vol 18 (4) ◽  
pp. 92-101
Author(s):  
V. A. Petrov ◽  
V. M. Alifirova ◽  
I. V. Saltykova ◽  
I. A. Zhukova ◽  
N. G. Zhukova ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gut Microbiota ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Background. Nowadays many efforts are taken in searching for Parkinson’s disease biomarkers, especially for an early recognition of the disease. The gut microbiota is one of the potential sources of biomarkers, changes in the composition of which in PD are actively studied.The aim of this study is to identify microbiota biomarkers in the Parkinson’s disease with an estimated accuracy of the diagnostics, including differential diagnostics, relative to other neurological diseases for patients of the Russian population.Material and methods. One hundred ninety-two metagenomics profiles from patients with Parkinson’s disease (n = 93), people with other neurological diagnoses (n = 33), and healthy controls (n = 66) were included in this study. These profiles were obtained with amplicon sequencing of bacterial 16S rRNA genes. Classifying models were made using the naive Bayes classifier, the artificial neural network, support vector machine, generalized linear model, and partial least squares regression.As a result we established that an optimal classification by the composition of the gut microbiota on the validation sample (sensitivity 91.30%, specificity 91.67% at 91.49% accuracy) amid patients was demonstrated with a naive Bayes classifier using the representation of the following genera as predictors: Christensenella, Methanobrevibacter, Leuconostoc, Enterococcus, Catabacter, Desulfovibrio, Sphingomonas, Yokenella, Atopobium, Fusicatenibacter, Cloacibacillus, Bulleidia, Acetanaerobacterium, and Staphylococcus.Conclusions. Information of the gut microbiota taxonomic composition may be used in differential diagnosis of Parkinson’s disease. 

scholarly journals Detection of Dermal Alpha-Synuclein Deposits as a Biomarker for Parkinson’s Disease

2021 ◽  
pp. 1-11
Author(s):  
Kathrin Doppler
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immunohistochemical Staining ◽  
Outcome Measure ◽  
Alpha Synuclein ◽  
Good Sensitivity ◽  
Promising Tool ◽  
Skin Biopsies ◽  
Methodological Problems ◽  
Sensitivity Specificity

Alpha-synuclein deposits are detectable in skin biopsies of patients with Parkinson’s disease and other synucleinopathies like multiple system atrophy by immunohistochemical staining. As they are easily to obtain, they appear a promising tool for the pre-mortem histopathological confirmation of the disease and as a potential outcome measure in studies targeting alpha-synuclein aggregates. Good sensitivity, specificity, and practicability are the most important requirements of a biomarker. The review gives an overview on all three aspects, addresses methodological problems and the lack of standardized procedures as a major problem and gives an outlook on the future of skin biopsy as a potential diagnostic tool in synucleinopathies.

scholarly journals Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Qingchao Liu ◽  
Jian Lu ◽  
Shuyan Chen ◽  
Kangjia Zhao
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classifier Ensemble ◽  
Optimal Threshold ◽  
Incident Detection ◽  
Bayes Classifier ◽  
Traffic Incident ◽  
Better Than

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

scholarly journals Coordinating a new approach to basic research into Parkinson’s disease

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Randy Schekman ◽  
Ekemini AU Riley
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basic Research ◽  
Open Science ◽  
Research Community ◽  
International Network ◽  
New Approach ◽  
Science Practices ◽  
New Therapies

The Aligning Science Across Parkinson’s (ASAP) initiative is building an international network of researchers to improve our understanding of the biology underlying Parkinson's disease. Developing a better understanding of how the disease originates and progresses will, we hope, lead to new therapies. The ASAP initiative will incentivize collaboration between the existing PD research community and other researchers and will be committed to open-science practices.

scholarly journals Communication Sentiment Analyzer using Machine Learning with Naive Bayes Bernoullinb

2019 ◽  
Vol 9 (1) ◽  
pp. 5976-5979
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Major Part ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
User Preferences ◽  
Social Media Data ◽  
Machine Learning Model ◽  
The World ◽  
Media Data

In this never-ending social media era it is estimated that over 5 billion people use smartphones. Out of these, there are over 1.5 billion active users in the world. In which we all are a major part and before opening our messages we all are curious about what message we have received. No doubt, we all always hope for a good message to be received. So Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Finally, we propose a scalable machine learning model to analyze the polarity of a communicative text using Naive Bayes’ Bernoulli classifier. This paper works on only two polarities that is whether the sentence is positive or negative. Bernoulli classifier is used in this paper because it is best suited for binary inputs which in turn enhances the accuracy of up to 97%.

Sign in / Sign up

Export Citation Format

Share Document