Efficient diagnosis system for Parkinson's disease using deep belief network

Al-Fatlawi, Ali; Jabardi, Mohammed; Ling, Sai Ho

doi:10.1109/cec.2016.7743941

Cited by 74 publications

(37 citation statements)

References 28 publications

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“…A deep belief network (DBN) has been used in [111] for the diagnosis of Parkinson's disease (PD) based on the speech signals obtained from the UCI repository. The proposed method was trained on various healthy and patient voices, and feature extraction was performed by inputting DBN.…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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“…DBN [9] is composed of multiple RBMs. The hidden layer of the first layer RBM is used as the display layer of the second RBM.…”

Section: Performance Analysis Fitting Model Based On Dbnmentioning

confidence: 99%

Effectiveness analysis of ship formation air defence based on deep belief network

Luo

Wang

et al. 2020

J. eng.

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“…Method Accuracy (%) [38] PCA-AE-Ada 85 [39] ACO-SVM 95.98 [35] GA-SVM 97.19 [35] PSO-SVM 97.37 [26] GA-MOO-NN 98.85 [14] PCA-SVM 96.84 [40] Breast cancer diagnosis techniques using SVM, PNN, and MLP 97.80 [11] Classification system using fuzzy-GA method 97.36 [20] Classification system using mixture ensemble of convolutional neural network 96.39 [41] SAE-SVM 98.25 [42] Prediction of breast cancer using SVM and K-NN 98.57 [43] Breast…”

Section: Referencementioning

confidence: 99%

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Memon

Haq

et al. 2019

Wireless Communications and Mobile Computing

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The accurate and efficient diagnosis of breast cancer is extremely necessary for recovery and treatment in early stages in IoT healthcare environment. Internet of Things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role of artificial intelligence and data mining techniques. The current state-of-the-art method does not effectively diagnose the breast cancer in the early stages, and most of the ladies suffered from this dangerous disease. Thus, the early detection of breast cancer significantly poses a great challenge for medical experts and researchers. To solve the problem of early-stage detection of breast cancer, we proposed machine learning-based diagnostic system which effectively classifies the malignant and benign people in the environment of IoT. In the development of our proposed system, a machine learning classifier support vector machine is used to classify the malignant and benign people. To improve the classification performance of the classification system, we used a recursive feature selection algorithm to select more suitable features from the breast cancer dataset. The training/testing splits method is applied for training and testing of the classifier for the best predictive model. Additionally, the classifier performance has been checked on by using performance evaluation metrics such as classification, specificity, sensitivity, Matthews’s correlation coefficient, F1-score, and execution time. To test the proposed method, the dataset “Wisconsin Diagnostic Breast Cancer” has been used in this research study. The experimental results demonstrate that the recursive feature selection algorithm selects the best subset of features, and the classifier SVM achieved optimal classification performance on this best subset of features. The SVM kernel linear achieved high classification accuracy (99%), specificity (99%), and sensitivity (98%), and the Matthews’s correlation coefficient is 99%. From these experimental results, we concluded that the proposed system performance is excellent due to the selection of more appropriate features that are selected by the recursive feature selection algorithm. Furthermore, we suggest this proposed system for effective and efficient early stages diagnosis of breast cancer. Thus, through this system, the recovery and treatment will be more effective for breast cancer. Lastly, the implementation of the proposed system is very reliable in all aspects of IoT healthcare for breast cancer.

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Efficient diagnosis system for Parkinson's disease using deep belief network

Cited by 74 publications

References 28 publications

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Effectiveness analysis of ship formation air defence based on deep belief network

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

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