A new approach: information gain algorithm-based k-nearest neighbors hybrid diagnostic system for Parkinson’s disease

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

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

eLife ◽

10.7554/elife.51167 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 4

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.

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Early Detection of Parkinson's Disease

Early Detection of Neurological Disorders Using Machine Learning Systems - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8567-1.ch005 ◽

2019 ◽

pp. 61-95

Author(s):

Debashree Devi ◽

Saroj K. Biswas ◽

Biswajit Purkayastha

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Early Detection ◽

Neurodegenerative Disorder ◽

Diagnostic System ◽

Diagnostic Model ◽

Neural Signals ◽

Learning Machine ◽

Preprocessing Technique ◽

Thalamic Region

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.

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A New Approach to Assessing the Extent of Degradation of the Nigrostriatal Dopaminergic System in an Experimental Model of Parkinson’s Disease

Neuroscience and Behavioral Physiology ◽

10.1007/s11055-020-00920-3 ◽

2020 ◽

Vol 50 (4) ◽

pp. 451-458

Author(s):

V. V. Safandeev ◽

M. V. Ugrumov

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Experimental Model ◽

Dopaminergic System ◽

New Approach

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Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

Scientific Reports ◽

10.1038/s41598-019-53656-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Rana Zia Ur Rehman ◽

Silvia Del Din ◽

Yu Guan ◽

Alison J. Yarnall ◽

Jian Qing Shi ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Clinical Decision Making ◽

Information Gain ◽

Step Length ◽

Support Vector ◽

Step Width ◽

Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

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Optimal Classifier of Parkinson’s Disease based on features selected by Information Gain in 3D Gait Analysis for Differential Diagnosis

Gait & Posture ◽

10.1016/j.gaitpost.2017.06.372 ◽

2017 ◽

Vol 57 ◽

pp. 205-206 ◽

Cited By ~ 1

Author(s):

Ilaria Bortone ◽

Gianpaolo Francesco Trotta ◽

Giacomo Donato Cascarano ◽

Alberto Argentiero ◽

Nadia Agnello ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Differential Diagnosis ◽

Gait Analysis ◽

Information Gain ◽

Optimal Classifier

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Intraoperative functional MRI as a new approach to monitor deep brain stimulation in Parkinson's disease

European Radiology ◽

10.1007/s00330-003-2051-0 ◽

2004 ◽

Vol 14 (4) ◽

pp. 686-690 ◽

Cited By ~ 29

Author(s):

Bettina Sorger ◽

Ralf Girnus ◽

Oliver Schulte ◽

Barbara Krug ◽

Klaus Lackner ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Brain Stimulation ◽

Functional Mri ◽

Brain Stimulation ◽

New Approach ◽

Deep Brain

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New Approach of Features Extraction for Numeral Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416500142 ◽

2016 ◽

Vol 30 (06) ◽

pp. 1650014 ◽

Cited By ~ 1

Author(s):

Soukaina Benchaou ◽

M’Barek Nasri ◽

Ouafae El Melhaoui

Keyword(s):

Statistical Method ◽

Recognition Rate ◽

Nearest Neighbors ◽

Features Extraction ◽

Contour Detection ◽

Statistical Techniques ◽

K Nearest Neighbors ◽

New Approach ◽

Attribute Vector ◽

Numeral Recognition

This paper proposes a new approach of features extraction based on structural and statistical techniques for handwritten, printed and isolated numeral recognition. The structural technique is inspired from the Freeman code, it consists first of contour detection and closing it by morphological operators. After that, the Freeman code was applied by extending its directions to 24-connectivity instead of 8-connectivity. Then, this technique is combined with the statistical method profile projection to determine the attribute vector of the particular numeral. Numeral recognition is carried out in this work through k-nearest neighbors and fuzzy min-max classification. The recognition rate obtained by the proposed system is improved indicating that the numeral extracted features contain more details.

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