scholarly journals DETECTION OF SEIZURE TYPES FROM THE WAVELET ENERGY OF SCALP EEG

2021 ◽  
Vol 57 (2) ◽  
pp. 340-349
Author(s):  
Joseph Mathew ◽  
◽  
N. Sivakumaran ◽  
P.A. Karthick
Keyword(s):  
Nearest Neighbor ◽  
Clinical Manifestations ◽  
University Hospital ◽  
Classification Model ◽  
Medical Attention ◽  
K Nearest Neighbor ◽  
Scalp Eeg ◽  
Wavelet Energy ◽  
Significant Difference ◽  
Life Threatening

Epilepsy is a disabling and devastating neurological disorder, characterized by recurrent seizures. These seizures are caused by the abrupt disturbance of the brain and are categorized into various types based on the clinical manifestations and localization. Seizures with clinical manifestations require immediate medical attention. In this work, an attempt has been made to differentiate the seizures with and without clinical manifestations using wavelet energy of scalp EEG signals. For this purpose, scalp EEG records from the publically available Temple University Hospital (TUH) database are considered in this work. The first four seconds of scalp EEG during seizure is subjected to seven-level Daubechies (db4) wavelet decomposition and energy is extracted from the resultant coefficients. These features are used to develop k-Nearest Neighbor (k-NN) classification model for the detection. The results show that the energy associated with most of the sub-bands exhibits significant difference (p<0.05) in these two types of seizures. It is found that the machine learning model based on k-NN achieves an accuracy of 87.6% and precision of 87.3%. Therefore, it appears that the proposed approach could aid in detecting life-threatening seizures in clinical settings.

scholarly journals Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongyan Wang
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Test Score ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Classification Efficiency

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.

scholarly journals A MODIFIED MODEL BASED ON FLOWER POLLINATION ALGORITHM AND K-NEAREST NEIGHBOR FOR DIAGNOSING DISEASES

2018 ◽  
Vol 19 (1) ◽  
pp. 144-157
Author(s):  
Mehdi Zekriyapanah Gashti
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Critical Role ◽  
Clinical Manifestations ◽  
Flower Pollination Algorithm ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Modified Model ◽  
Flower Pollination

Exponential growth of medical data and recorded resources from patients with different diseases can be exploited to establish an optimal association between disease symptoms and diagnosis. The main issue in diagnosis is the variability of the features that can be attributed for particular diseases, since some of these features are not essential for the diagnosis and may even lead to a delay in diagnosis. For instance, diabetes, hepatitis, breast cancer, and heart disease, that express multitudes of clinical manifestations as symptoms, are among the diseases with higher morbidity rate. Timely diagnosis of such diseases can play a critical role in decreasing their effect on patients’ quality of life and on the costs of their treatment. Thanks to the large data set available, computer aided diagnosis can be an advanced option for early diagnosis of the diseases. In this paper, using a Flower Pollination Algorithm (FPA) and K-Nearest Neighbor (KNN), a new method is suggested for diagnosis. The modified model can diagnose diseases more accurately by reducing the number of features. The main purpose of the modified model is that the Feature Selection (FS) should be done by FPA and data classification should be performed using KNN. The results showed higher efficiency of the modified model on diagnosis of diabetes, hepatitis, breast cancer, and heart diseases compared to the KNN models. ABSTRAK: Pertumbuhan eksponen dalam data perubatan dan sumber direkodkan daripada pesakit dengan penyakit berbeza boleh disalah guna bagi membentuk kebersamaan optimum antara simptom penyakit dan mengenal pasti gejala penyakit (diagnosis). Isu utama dalam diagnosis adalah kepelbagaian ciri yang dimiliki pada penyakit tertentu, sementara ciri-ciri ini tidak penting untuk didiagnosis dan boleh mengarah kepada penangguhan dalam diagnosis. Sebagai contoh, penyakit kencing manis, radang hati, barah payudara dan penyakit jantung, menunjukkan banyak klinikal simptom jelas dan merupakan penyakit tertinggi berlaku dalam masyarakat. Diagnosis tepat pada penyakit tersebut boleh memainkan peranan penting dalam mengurangkan kesan kualiti  hidup dan kos rawatan pesakit. Terima kasih kepada set data yang banyak, diagnosis dengan bantuan komputer boleh menjadi pilihan maju menuju ke arah diagnosis awal kepada penyakit. Kertas ini menggunakan Algoritma Flower Pollination (FPA) dan K-Nearest Neighbor (KNN), iaitu kaedah baru dicadangkan bagi diagnosis. Model yang diubah suai boleh mendiagnosis penyakit lebih tepat dengan mengurangkan bilangan ciri-ciri. Tujuan utama model yang diubah suai ini adalah bagi Pemilihan Ciri (FS) perlu dilakukan menggunakan FPA and pengkhususan data perlu dijalankan menggunakan KNN. Keputusan menunjukkan model yang diubah suai lebih cekap dalam mendiagnosis penyakit kencing manis, radang hati, barah payudara dan penyakit jantung berbanding model KNN.

Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS

2010 ◽  
Vol 44-47 ◽  
pp. 1130-1134
Author(s):  
Sheng Li ◽  
Pei Lin Zhang ◽  
Bing Li
Keyword(s):  
Feature Selection ◽  
Hydraulic System ◽  
Nearest Neighbor ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Faults Diagnosis

Feature selection is a key step in hydraulic system fault diagnosis. Some of the collected features are unrelated to classification model, and some are high correlated to other features. These features are harmful for establishing classification model. In order to solve this problem, genetic algorithm-partial least squares (GA-PLS) is proposed for selecting the representative and optimal features. K nearest neighbor algorithm (KNN) is used for diagnosing and classifying hydraulic system faults. For expressing better performance of GA-PLS, the original data of a model engineering hydraulic system is used, and the results of GA-PLS are compared with all feature used and GA. The experimental results show that, the proposed feature method can diagnose and classify hydraulic system faults more efficiently with using fewer features.

scholarly journals An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽  
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.

scholarly journals Transfer Learning-Based Search Model for Hot Pepper Diseases and Pests

Agriculture ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 439 ◽  
Author(s):  
Helin Yin ◽  
Yeong Hyeon Gu ◽  
Chang-Jin Park ◽  
Jong-Han Park ◽  
Seong Joon Yoo
Keyword(s):  
Nearest Neighbor ◽  
Image Data ◽  
Performance Comparison ◽  
Classification Model ◽  
Search Model ◽  
Hot Pepper ◽  
Final Decision ◽  
Image Search ◽  
K Nearest Neighbor ◽  
Search Results

The use of conventional classification techniques to recognize diseases and pests can lead to an incorrect judgment on whether crops are diseased or not. Additionally, hot pepper diseases, such as “anthracnose” and “bacterial spot” can be erroneously judged, leading to incorrect disease recognition. To address these issues, multi-recognition methods, such as Google Cloud Vision, suggest multiple disease candidates and allow the user to make the final decision. Similarity-based image search techniques, along with multi-recognition, can also be used for this purpose. Content-based image retrieval techniques have been used in several conventional similarity-based image searches, using descriptors to extract features such as the image color and edge. In this study, we use eight pre-trained deep learning models (VGG16, VGG19, Resnet 50, etc.) to extract the deep features from images. We conducted experiments using 28,011 image data of 34 types of hot pepper diseases and pests. The search results for diseases and pests were similar to query images with deep features using the k-nearest neighbor method. In top-1 to top-5, when using the deep features based on the Resnet 50 model, we achieved recognition accuracies of approximately 88.38–93.88% for diseases and approximately 95.38–98.42% for pests. When using the deep features extracted from the VGG16 and VGG19 models, we recorded the second and third highest performances, respectively. In the top-10 results, when using the deep features extracted from the Resnet 50 model, we achieved accuracies of 85.6 and 93.62% for diseases and pests, respectively. As a result of performance comparison between the proposed method and the simple convolutional neural network (CNN) model, the proposed method recorded 8.62% higher accuracy in diseases and 14.86% higher in pests than the CNN classification model.

scholarly journals The enzymopathy of G6PD deficiency in Jordan: a demographic and biochemical analysis

2018 ◽  
Vol 10 (1) ◽  
pp. 25-32
Author(s):  
Ahmed Al-Imam
Keyword(s):  
G6pd Deficiency ◽  
Liver Enzymes ◽  
Clinical Manifestations ◽  
University Hospital ◽  
Drug Induced ◽  
Mean Values ◽  
Significant Difference ◽  
Wbc Count ◽  
Glucose 6 Phosphate Dehydrogenase ◽  
Blood Grouping

Background: G6PD deficiency is an inherited X-linked recessive condition leading to insufficient levels of glucose-6-phosphate dehydrogenase, thus causing hemolytic anaemia under certain circumstances. Materials and Methods: Our study is explorative for cases admitted to Jordan University Hospital. The studied parameters include demographics, clinical manifestations, biochemical markers including Hb level, WBC count, liver enzymes, and blood grouping. Results: Most of the patients were admitted to the emergency unit (53.13%). Individuals who were Rh-positive represented 57.81%, while patients of AB blood group accounted for 75%. The mean values were 4.81 years (age), 29.06 hours (time-to-hospital admission), 38.10 degree Celsius (temperature), 6.11 gm/dl (Hb), 13242.19 (WBC count), 343.20 U/L (S. ALP), and 50.98 IU/L (S. ALT). There was no significant difference between males and females or between favism-induced versus drug-induced hemolytic episodes. AB and Rh positive blood groups are of a protective effect in relation to liver enzymes. Patients who were admitted to the hospital within 24 hours from having clinical manifestations had a better prognosis. Conclusion: This study is the first inferential research on G6PD deficiency from the Middle East to explore cases from one of the largest healthcare centres in Jordan. The role of blood grouping should be investigated prospectively.

Clinical Significance of Cryofibrinogenemia: Possible Pathophysiological Link with Raynaud’s Phenomenon

2011 ◽  
Vol 39 (1) ◽  
pp. 119-124 ◽  
Author(s):  
MUHAMMAD S. SOYFOO ◽  
AHMED GOUBELLA ◽  
ELIE COGAN ◽  
JEAN-CLAUDE WAUTRECHT ◽  
ANNICK OCMANT ◽  
...  
Keyword(s):  
Raynaud’S Phenomenon ◽  
Clinical Signs ◽  
Clinical Manifestations ◽  
Raynaud's Phenomenon ◽  
Clinical Findings ◽  
University Hospital ◽  
Significant Difference ◽  
Primary Raynaud’S Phenomenon ◽  
The Mean ◽  
Mean Concentration

Objective.To describe the clinical findings and prevalence of patients with cryofibrinogenemia (CF) and to determine whether CF is associated with primary Raynaud’s phenomenon.Methods.Between June 2006 and December 2009, 227 patients were tested for CF in a single university hospital. Forty-five patients with primary Raynaud’s phenomenon were tested for CF.Results.A total of 117 patients with CF without cryoglobulinemia were included. The main clinical manifestations included skin manifestations (50%) and arthralgia (35%). There were 67 patients with primary CF and 50 patients with secondary CF. There was no significant difference in the mean concentration of the cryoprecipitate in primary CF as compared to the secondary form (172 ± 18.6 vs 192 ± 20.9 mg/dl, respectively; p = 0.41). Highest concentrations of cryoprecipitate were observed in those containing fibrinogen only as compared to cryoprecipitates containing fibrinogen and fibronectin (301 ± 43.5 vs 125 ± 10.6 mg/dl; p < 0.001). Patients having skin necrosis (n = 3) had significantly higher values of cryofibrinogen compared to those without necrosis (638 ± 105 vs 160 ± 10.2 mg/dl; p = 0.0046). Among the 45 patients with primary Raynaud’s phenomenon, 36 had associated CF. There was no significant difference in the mean concentration of the cryoprecipitate in these patients compared to those with primary CF.Conclusion.There seems to be a significant correlation between cryofibrinogen concentration and the severity of the clinical signs, particularly when cryoprecipitate is composed of fibrinogen alone. CF might have a possible pathophysiological role in primary Raynaud’s phenomenon.

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

2021 ◽  
Author(s):  
Monika Jyotiyana ◽  
Nishtha Kesswani ◽  
Munish Kumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Health Sector ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Linear Discriminant

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.

scholarly journals Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Muhammad Irfan ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
Pregnancy Risk ◽  
The Public ◽  
Risk Monitoring ◽  
Machine Learning Model ◽  
Union Operation ◽  
Correlation Based Feature Selection

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.

scholarly journals Chronic Disease Prediction Using Effective Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 1211-1216
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Predictive Analytics ◽  
Medical Condition ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Medical Sector ◽  
Build Time ◽  
Life Threatening ◽  
Precautionary Measures

Healthcare is a major sector where there is demand for predictive analytics using machine learning. Healthcare will be largely benefited when useful knowledge can be transferred into timely action to manage hazardous situations in medical sector. Chronic kidney disease is a life threatening disease which can be prevented with timely right predictions and appropriate precautionary measures. In this paper, various machine learning classifiers are applied on the medical dataset to develop a prediction model to tell if a person's present medical condition can lead to the chronic stage of the disease in future. The higher prediction accuracy and decreased build time is obtained with reduced feature set attributes by applying Best First and Greedy stepwise algorithm combined with different classification techniques like Naive Bayes ,Support vector machine (SVM), J48, Random Forest, and K Nearest Neighbor(KNN).

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