Classification and prediction of Alzheimer's disease using multi-layer perceptron

Jyotiyana, Monika; Kesswani, Nishtha

doi:10.1504/ijris.2020.10033984

Cited by 2 publications

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“…Machine learning techniques [18] have become increasingly popular due to their capability of giving accurate results in problems such as classification and clustering. A broad classification of different Machine learning techniques is given in Figure 1.…”

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confidence: 99%

A deep learning approach for classification and diagnosis of Parkinson’s disease

2022

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Deep learning techniques are playing an important role in the classification and prediction of diseases.Undoubtedly deep learning has a promising future in the health sector, especially in medical imaging.The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating reports. In this paper, we have proposed a Deep Neural Network-based classification model for Parkinson's disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson's disease patients with excellent accuracy of 94.87%. Based on the attributes of the dataset of the patient, the model can be used for the identification of Parkinsonism's. We have also compared the results with other existing

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Section: Introductionmentioning

confidence: 99%

A deep learning approach for classification and diagnosis of Parkinson’s disease

2022

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A Deep Learning Approach for Classification and Diagnosis of Parkinson’s Disease

Jyotiyana

Kesswani

Kumar

2021

Preprint

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Deep learning techniques are playing an important role in the classification and prediction of diseases. Undoubtedly deep learning has a promising future in the health sector, especially in medical imaging. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating reports. In this paper, we have proposed a Deep Neural Network-based classification model for Parkinson’s disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson’s disease patients with excellent accuracy of 94.87%. Based on the attributes of the dataset of the patient, the model can be used for the identification of Parkinsonism's. We have also compared the results with other existing approaches like Linear Discriminant Analysis, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Classification and Regression Trees, Random Forest, Linear Regression, Logistic Regression, Multi-Layer Perceptron, and Naive Bayes.

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Classification and prediction of Alzheimer's disease using multi-layer perceptron

Cited by 2 publications

References 0 publications

A deep learning approach for classification and diagnosis of Parkinson’s disease

A deep learning approach for classification and diagnosis of Parkinson’s disease

A Deep Learning Approach for Classification and Diagnosis of Parkinson’s Disease

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