Classifying And Predicting Adolescent Cardiac Health Using KNN

ABSTRAK Plak ateroklerosis menyebabkan penyakit jantung koroner, yang menjadi penyebab kematian kedua di negara berkembang. Penyakit jantung koroner menyebabkan 17,9 juta kematian pada tahun 2016—31% dari semua kematian di seluruh dunia. Lebih dari tiga perempat kasus kematian di negara berkembangan disebabkan oleh penyakit jantung dan pembuluh darah. Aktivitas fisik yang berlebihan, stres, udara yang dingin, pola hidup yang kurang sehat, dan riwayat penyakit jantung dari keluarga adalah faktor risiko penyakit jantung koroner. Risiko penyakit jantung koroner meningkat pada penderita yang memiliki banyak faktor risiko penyakit jantung. Oleh karena itu, individu dengan faktor risiko ini disarankan melakukan deteksi dini dengan pemeriksaan rekam jantung secara berkala untuk mencegah dan apabila ditemukan gangguan irama jantung, penderita dapat melakukan pemeriksaan lanjutan berdasarkan gejala klinis dan hasil rekam jantung. Kegiatan pengabdian bertujuan untuk deteksi dini penyakit jantung koroner melalui pemeriksaan rekam jantung pada sivitas akademika Universitas Khairun. Metode yang dilakukan adalah pemeriksaan rekam jantung dan melakukan tanya jawab terkait keluhan yang dirasakan saat diperiksa. Hasil yang didapatkan dari kegiatan ini tedapat 14 orang yang memiliki hasil rekam jantung yang bervariasi. Dari kegiatan perlu adanya pemantauan berkala terhadap risiko penyakit jantung koroner agar dapat mencegah terjadinya penyakit jantung dan meningkatkan kualitas kerja sivitas akademika Universitas Khairun. Kata Kunci: Deteksi Dini, Penyakit Jantung Koroner, Pemeriksaan Rekam Jantung ABSTRACT Plaque atherosclerosis causes coronary heart disease, which is the second leading cause of death in developing countries. Coronary heart disease caused 17.9 million deaths in 2016-31% of all deaths worldwide. More than three-quarters of deaths in developing countries are caused by heart and vascular diseases. Excessive physical activity, stress, cold weather, unhealthy lifestyle, and family history of heart disease are risk factors for coronary heart disease. The risk of coronary heart disease increases in patients who have many risk factors for heart disease. Therefore, individuals with these risk factors are advised to do early detection with regular heart record checks to prevent and if heart rhythm disturbances are found, patients can undergo further examination based on clinical symptoms and heart record results. The service activity aims to early detection of coronary heart disease through electrocardiogram checks in the academic community of Khairun University. The method used was heart record examination and questioning related to complaints felt when examined. The results obtained from this activity were 14 people who had varying electrocardiogram results. From the activity, it is necessary to periodically monitor the risk of coronary heart disease in order to prevent the occurrence of heart disease. Keywords: Early Detection, Coronary Heart Disease, Electrocardiogram Examination

Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

Spectrum sensing use current primary user (PU) received signal to detect PU channel state at current time, however PU channel state in near future using previous detected channel state information can secure an efficient utilization of unoccupied spectrum, also can help in preventing harmful interference with PU and solving problem of latency between spectrum sensing and data transmission which caused by hardware implementation. In this paper, an algorithm Based on K-Nearest Neighbor (KNN) Classifier and Kalman Filter is proposed to detect and predict future of primary user channel state the spectrum holes. The new algorithm based on training KNN classifier over a set containing some frame time slots energy test statistics along with their corresponding decisions about presence or absence of primary user transmission, so as to predict decisions for new unclassified time slot, then utilize Kalman filter to track PU primary user channel state by next expected spectrum holes based on previously detected hole. Experimental results show that proposed approach is efficient and easy to implement for detecting and tracking predicting PU channel state. KEYWORD: Channel state tracking; Channel state prediction; K-nearest neighbor classifier; Kalman Filter; Spectrum sensing time slot

Recently, computer and smartphone devices have been increasingly employed in various life aspects. Human Activity Recognition (HAR) is one of the most emerging fields that can be clearly noticed in terms of computer related researches. This study proposes a classification model to recognize human six basic activities based on smartphone embedded sensors. For feature selection, we utilized three optimization search algorithms which are: Particle Swarm Optimization (PSO), Greedy, and Genetic algorithm. K-nearest neighbors (KNN) classifier, with Manhattan distance function, is adopted to measure the models performance on two publicly available well-known datasets (i.e., WISDM and UCI-HAR). All experiments are achieved using 10-folds cross validation technique, and the results are evaluated and compared using different metrics, for instance, accuracy, root mean square error, f-measure, and number of features. The experimental results proved that PSO based KNN classifier presents the best performance compared to the other two algorithms, in addition to the baseline KNN classifier. Moreover, the proposed model outperforms those state-of-the-art models presented in related literature regarding the UCI-HAR dataset with performance rate of 98.89%, and close to the best performance achieved on WISDM dataset with performance rate of 91.07%.

While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem. To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN) classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier. The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.

Underwater garbage affects the life of marine creatures and the entire ecosystem. Detecting underwater garbage is an important research area. In this study, a method is proposed to detect underwater garbage. The open-access Trash-ICRA19 dataset was used to implement the proposed method. The data set cropping process was applied and a data set consisting of 11060 images in total was obtained. These images were converted to 200×200 pixels using preprocessing. By applying the Directed Gradient Histogram (HOG) algorithm, 11060×900 feature vectors were obtained. The resulting feature vectors were then calculated using KNN (K Nearest Neighbor Algorithm), DT (Decision Tree), LD (Linear Discriminant), NB (Naive Bayes), and SVM (Support Vector Machine) classifiers. The results obtained showed that 97.78% accuracy was obtained when the KNN classifier was used in this method. The use of only feature extractors and classifiers in the proposed method shows that the method is lightweight. It has low computational complexity compared to existing studies in the literature. Moreover, according to its performance results, it is more successful than the methods in the literature.

This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) classifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power spectrum in term of Energy Spectral Density (ESD) for each frequency bands (Delta, Theta, Alpha and Beta) was calculated. The ratio of EEG power spectrum and the average value of Spectral Centroids were selected as features to k-Nearest Neighbor (k-NN). The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The results showed that the combination of EEG power spectrum and Spectral Centroids techniques with the training and testing of k-NN set at 70:30 able to detect and classify the unique features for human stress at 88.89% accuracy.

The K-Nearest Neighbour (KNN) classification is a standard method in pattern recognition and is especially marked out by its simplicity [128, 148, 305, 370, 374, 379, 389].

To examine the associations between physical activity and a wide range of risk factors for ischaemic heart disease including fibrinogen concentration and viscosity. Cross sectional evidence from the 2398 men aged 50-64 years in the Caerphilly Prospective Heart Disease Study. Validated questionnaires were used to quantify energy expenditure on leisure activities and to grade activities related to occupation. Risk factors for heart disease examined included blood pressure, lipids, fibrinogen, and plasma viscosity. Possible confounding variables included smoking, employment, and prevalent heart disease (angina, previous myocardial infarction, and electrocardiographic evidence of ischaemia). Fibrinogen concentration was lower by 0.24 g/l and viscosity by 0.026 cP in the third of men who were most active in leisure activities (about 0.25 x 1 SD). A weak positive relation was found with high density lipoprotein cholesterol, but none with total cholesterol or fasting glucose concentrations or blood pressure. Triglyceride concentrations seem to be substantially lower in the most active men, although the evidence for this is not consistent. Work related activity showed relation with the lipid concentration but not with the haemostatic tests. Leisure activities of all levels seem to affect haemostatic and lipid factors beneficially. These effects correspond to a difference in the risk of heart disease for an active man and a sedentary man of at least 7% or 8%. Fasting triglyceride concentrations have already been shown to be strongly predictive of heart disease in this cohort of men, and the effect of exercise on this factor is also likely to confer benefit.

This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.

Attentional bias in MDD: ERP components analysis and classification using a dot-probe task

In this paper, we propose a novel framework of localization algorithm based on Compressive Sensing (CS) by K-Nearest Neighbor (KNN) classification in Wireless Sensor Networks (WSNs), which can effectively alleviate the problem of off-grid. Many papers about CS-based localization assume that each target is in the center of the grid. However, the assumption is almost impossible. In this paper, the targets can be located anywhere in the area. The target location is regarded as an unknown sparse signal in the discrete spatial domain. Practically, the recovered discrete signal is not exactly K-sparse. In order to make high level of localization accuracy, we localize the targets in three different situations and choose the cluster region according to the distribution of the power of the targets. Then for each classification, we obtain the final location by KNN. This approach can make great use of the location information and compensate the error induced by the problem of off-grid. Finally, simulation and performance analysis show the effectiveness and robustness of our proposed localization algorithm.

PurposeThis work aimed to improve breast screening program accuracy using automated classification. The goal was to determine if whole image features represented in the discrete cosine transform would provide a basis for classification. Priority was placed on avoiding false negative findings.MethodsOnline datasets were used for this work. No informed consent was required. Programs were developed in Mathematica and, where necessary to improve computational performance ported to C++. The use of a discrete cosine transform to separate normal from cancerous breast tissue was tested. Features (moments of the mean) were calculated in square sections of the transform centered on the origin. K-nearest neighbor and naive Bayesian classifiers were tested.ResultsForty-one features were generated and tested singly, and in combination of two or three. Using a k-nearest neighbor classifier, sensitivities as high as 98% with a specificity of 66% were achieved. With a naive Bayesian classifier, sensitivities as high as 100% were achieved with a specificity of 64%.ConclusionWhole image classification based on discrete cosine transform (DCT) features was effectively implemented with a high level of sensitivity and specificity achieved. The high sensitivity attained using the DCT generated feature set implied that these classifiers could be used in series with other methods to increase specificity. Using a classifier with near 100% sensitivity, such as the one developed in this project, before applying a second classifier could only boost the accuracy of that classifier.

In this study, an effective diagnostic method for wiring networks based on reflectometry technique, the K-Nearest Neighbour (KNN) classifier, and Dynamic Time Warping (DTW) was developed. The proposed approach relies on a two-fold process: the offline process and the online process. In the offline process, basic circuit elements-based modelling and the Finite-Difference Time-Domain (FDTD) numerical method are employed to simulate Time Domain Reflectometry (TDR) and generate necessary datasets simultaneously. These datasets are then used to train and obtain classification and regression models. The DTW distance is combined with the KNN classifier to derive these models. In the online process, the models are utilised to identify, locate, and characterise faults in Wiring Networks Under Test based on their TDR response. Numerical and experimental results are presented to illustrate the performance and feasibility of the proposed method.