A Performance Comparison Based on Machine Learning Approaches to Distinguish Parkinson’s Disease from Alzheimer Disease Using Spatiotemporal Gait signals

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

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A Performance Comparison of Machine Learning Approaches on Intrusion Detection Dataset

2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) ◽

10.1109/icicv50876.2021.9388502 ◽

2021 ◽

Author(s):

Vaddeboyina Sri Manvith ◽

R Vijaya Saraswathi ◽

R Vasavi

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Performance Comparison ◽

Learning Approaches ◽

A Performance

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A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

E3S Web of Conferences ◽

10.1051/e3sconf/202130901008 ◽

2021 ◽

Vol 309 ◽

pp. 01008

Author(s):

P. Mounika ◽

S. Govinda Rao

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Early Stage ◽

Signs And Symptoms ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Machine Learning Methods ◽

Learning Techniques

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

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Machine Learning Approaches in Parkinson’s Disease

Current Medicinal Chemistry ◽

10.2174/0929867328999210111211420 ◽

2021 ◽

Vol 28 ◽

Author(s):

Annamaria Landolfi ◽

Carlo Ricciardi ◽

Leandro Donisi ◽

Giuseppe Cesarelli ◽

Jacopo Troisi ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Neurodegenerative Disorder ◽

Learning Algorithms ◽

Disease Diagnosis ◽

Machine Learning Algorithms ◽

Clinical Aspects ◽

Learning Approaches

Background:: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective:: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods:: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results:: the obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed, after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion:: Machine learning algorithms are computer-based statistical approaches which can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

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