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

Abstract 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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Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Current Drug Targets ◽

10.2174/1389450119666180809122244 ◽

2019 ◽

Vol 20 (5) ◽

pp. 488-500 ◽

Cited By ~ 6

Author(s):

Yan Hu ◽

Yi Lu ◽

Shuo Wang ◽

Mengying Zhang ◽

Xiaosheng Qu ◽

...

Keyword(s):

Machine Learning ◽

Drug Design ◽

Anticancer Drugs ◽

Nearest Neighbor ◽

Cost Effective ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Activity Prediction ◽

Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

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Detection of Ovarian Tumor Using Machine Learning Approaches A Review

10.46532/978-81-950008-1-4_103 ◽

2020 ◽

pp. 471-476

Author(s):

Gitanjali Wadhwa ◽

Mansi Mathur

Keyword(s):

Machine Learning ◽

Ovarian Cancer ◽

Deep Learning ◽

Nearest Neighbor ◽

Performance Metrics ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Female Sex Hormones

The important part of female reproductive system is ovaries. The importance of these tiny glands is derived from the production of female sex hormones and female gametes. The place of these ductless almond shaped tiny glandular organs is on just opposite sides of uterus attached with ovarian ligament. There are several reasons due to which ovarian cancer can arise but it can be classified by using different number of techniques. Early prediction of ovarian cancer will decrease its progress rate and may possibly save countless lives. CAD systems (Computer-aided diagnosis) is a noninvasive routine for finding ovarian cancer in its initial stages of cancer which can keep away patients’ anxiety and unnecessary biopsy. This review paper states us about how we can use different techniques to classify the ovarian cancer tumor. In this survey effort we have also deliberate about the comparison of different machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and deep learning techniques used in classification process of ovarian cancer. Later comparing the different techniques for this type of cancer detection, it gives the impression that Deep Learning Technique has provided good results and come out with good accuracy and other performance metrics.

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Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection

Diagnostics ◽

10.3390/diagnostics11061076 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1076

Author(s):

Muntasir Hoq ◽

Mohammed Nazim Uddin ◽

Seung-Bo Park

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Nearest Neighbor ◽

Neurodegenerative Disorder ◽

Imbalanced Data ◽

Principal Component ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Extreme Gradient Boosting

As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models.

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PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

BMC Bioinformatics ◽

10.1186/s12859-020-03870-2 ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Jun Meng ◽

Qiang Kang ◽

Zheng Chang ◽

Yushi Luan

Keyword(s):

Deep Learning ◽

Noncoding Rna ◽

Nearest Neighbor ◽

Short Term Memory ◽

Biological Activities ◽

Support Vector ◽

Multiple Perspectives ◽

K Nearest Neighbor ◽

Rna Sequences ◽

Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

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Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson’s Disease

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.640591 ◽

2021 ◽

Vol 15 ◽

Author(s):

Edgar Peña ◽

Tareq M. Mohammad ◽

Fedaa Almohammed ◽

Tahani AlOtaibi ◽

Shahpar Nahrir ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Short Duration ◽

Spectral Power ◽

Clinical State ◽

Support Vector ◽

Learning Approaches ◽

Factor Scores ◽

Subject Specific ◽

Motor Cortical

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.

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Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

10.21203/rs.2.22561/v1 ◽

2020 ◽

Author(s):

Nazrul Anuar Nayan ◽

Hafifah Ab Hamid ◽

Mohd Zubir Suboh ◽

Noraidatulakma Abdullah ◽

Rosmina Jaafar ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Nearest Neighbor ◽

Total Cholesterol Level ◽

Population Based ◽

Support Vector ◽

K Nearest Neighbor ◽

Cvd Risk ◽

Linear Discriminant ◽

Artificial Neural Network Ann

Abstract Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

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A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network

Sensors ◽

10.3390/s19122814 ◽

2019 ◽

Vol 19 (12) ◽

pp. 2814 ◽

Cited By ~ 2

Author(s):

Xiaoguang Liu ◽

Huanliang Li ◽

Cunguang Lou ◽

Tie Liang ◽

Xiuling Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Fall Detection ◽

Classification Performance ◽

Daily Activities ◽

Support Vector ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Parallel Channels

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

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The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

Computational Intelligence and Neuroscience ◽

10.1155/2019/5850830 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Renzhou Gui ◽

Tongjie Chen ◽

Han Nie

Keyword(s):

Neural Network ◽

Deep Learning ◽

Correlation Matrix ◽

Nearest Neighbor ◽

Learning Model ◽

Fmri Data ◽

Support Vector ◽

K Nearest Neighbor ◽

The Impact ◽

Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

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Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i07.13569 ◽

2020 ◽

Vol 16 (07) ◽

pp. 34

Author(s):

Nayan Nazrul Anuar ◽

Ab Hamid Hafifah ◽

Suboh Mohd Zubir ◽

Abdullah Noraidatulakma ◽

Jaafar Rosmina ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Nearest Neighbor ◽

Total Cholesterol Level ◽

Population Based ◽

Support Vector ◽

K Nearest Neighbor ◽

Cvd Risk ◽

Linear Discriminant ◽

Artificial Neural Network Ann

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

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An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽

10.3390/s20216365 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6365

Author(s):

Jung Hwan Kim ◽

Chul Min Kim ◽

Man-Sung Yim

Keyword(s):

Nearest Neighbor ◽

Nuclear Industry ◽

Classification Model ◽

Support Vector ◽

Insider Threat ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Nuclear Facilities ◽

Time Frequency ◽

Threat Mitigation

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

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