A NOVEL DEEP LEARNING NEURAL NETWORK SYSTEM FOR IMBALANCED HEART SOUNDS CLASSIFICATION

This study proposed a novel TFNNS method, which aimed to solve the imbalanced phonocardiogram (PCG) signals’ classification. TFFNS consisted of three submodules: HeartNet, 2D-Maps transformation, and TF-Mask augmentation. HeartNet, deep neural networks (CNNs), was designed to recognize the categories of PCG signals. In particular, on the basis of short-time Fourier transform and Mel filtering, 2D-Maps transformation was used to convert one-dimensional PCG into two-dimensional Savitzky-MFSC feature maps that were fed into HeartNet; TF-Mask augmentation was designed to augment the training datasets by randomly shielded Savitzky-MFSC maps in the domains of time and frequency. We trained our model on the PASCAL heart sounds’ datasets to classify three categories of heart sounds including normal, murmur, and extrasystole. We also evaluated and compared the model with the baselines on the consistent evaluation protocols. The experimental results showed that the proposed TFFNS method significantly promoted the performance of the PCG signals’ classification and exceeded the baselines by giving the mean precision of 94%, heart problem specificity of 99%, and discriminant power of 1.317.

Limited Formal Education and its Impact of Disease Profile of Older Persons of Rawalpindi

Increased life expectancy and low mortality rates are the major reason for the increasing number of the older population. Developed countries are not only facing this problem, but the number of developing countries are also increasing. Pakistan is also among those countries having a greater portion of the older population. Objective: The present study was focused on exploring the relationship between the educational achievement of older persons and their disease profile. Methods: A structured tool was developed to interview 384 older persons. Data were coded and analyzed in SPSS. Male and female participation was with ratio 70:30 while 53.9% sample was age 60-65 years. Results: Most of the respondents were illiterate, followed by primary, secondary, and matriculation degree holder elders. Hypertension, Heart problem, Diabetes, Arthritis, and Asthma issue are observed among older persons. Diabetes is the only disease reported by OPs with a qualification from illiterate to a Master degree with varied percentiles. Regression model [ y=5.0749+.0646x] with R Square = .0013. Conclusion: We conclude that a relationship exists among study variables but non-significantly while the value of R^2tells us how assertive you can be that each distinct variable has some correlation with the dependent variable, which is the important indicator.

Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes

Diabetes is a group of metabolic diseases which is indicated by the occurrence of hyperglycemia caused by abnormalities in insulin secretion in the body. Many deaths are caused by diabetes, if this disease is not treated immediately, diabetes can cause damage to other organs such as blindness, stores, heart problem and even kidney problem. A best method is needed in classifying diabetes in order to detect diabetes early. Research related to the classification of diabetes using several calcification methods has been done before. In this study, two classification methods were compared, namely decision tree and naïve Bayes. Measurement methods were carried out through cross validation. The results obtained from this study are the best algorithms among the two algorithms to determine diabetes sufferers

A Short Review on the Bioactive Constituents from Six Terminalia Species

Terminalia as a genus has received a great attraction to evaluate and examine the pharmacological potential having their medicinal properties. Different species under Terminalia genus have been used as herbal medicine with various formulations in the treatment of abdominal pain, cancer, cough, conjunctivitis, diarrea, heart problem, leproscopy, urinary tract infection, and sexual related diseases. These properties have been reported to express abundant biological characteristics for example antioxidant, antiparasitic, antibacterial, antifungal, antiviral, and anti-inflammatory. This review has constructed to solicitude the phytochemicals from the genus Terminalia. A total six species belongs to this genus such as Terminalia chebula, T. citrina, T. phanerophlebia, T. belerica, T. catappa, and T. arjuna have been studied and fifty-six phytochemicals with their chemical structures have been reported in this review. Terminalia chebula consists of a higher number of phytochemicals as compared to the other species. Bangladesh Pharmaceutical Journal 24(1): 76-82, 2021

The category of the heart in philosophy and religion: from mysticism to pragmatics

One of the extremely urgent problems of modern anthropology is the problem of the whole person, more precisely, the problem of the completeness of his nature. It includes many elements of a different nature, which raises the question of the existence of a center that controls human activity as an integral being. The author proceeds from the fact that such a coordinating center, in accordance with historical, philosophical and religious studies, is the human heart. The article analyzes the features of understanding the heart, presented in the philosophical and religious literature. Special attention is paid to the views of the Christian philosopher A. S. Pozov, who takes a clear position in revealing the fundamental role of the human heart in all the diversity of its existence. The functional elements of cardiac activity, which can be of practical importance, have been identified. A series of questions about the practical applications of heart problem research has been formulated. This is the role and significance of the heart in the transition to a digital society. These are, further, the ways and means of transformation of human nature and their justification. Finally, following I. A. Ilyin, we talk about the need to study the evolution of the forms of “heart contemplation.”

Performance Analysis of Supervised Machine Learning Algorithms on Medical Dataset

Machine learning (ML) algorithms are designed to perform prediction based on features. With the help of machine learning, system can automatically learn and improve by experience. Machine learning comes under Artificial intelligence. Machine learning is broadly categorized in two types: supervised and unsupervised. Supervised ML performs classification and unsupervised is for clustering. In present scenario, machine learning is used in various areas. It can be used for biometric recognition, hand writing recognition, medical diagnosis etc. In medical field, machine learning plays an important role in identifying diseases based on patient’s features. Presently,doctors use software application based on machine learning algorithm in various disease diagnosis like cancer, cardiac arrest and many more. In this paper we used an ensemble learning method to predict heart problem. Our study described the performance of ML algorithms by comparing various evaluating parameters such as F-measure, Recall, ROC, precision and accuracy. The study done with various combination ML classifiers such as, Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF) algorithm to predict heart problem. The result showed that by combining two ML algorithm, DT with NB, 81.1% accuracy was achieved. Simultaneously, the models like Support Vector machine (SVM), Decision tree, Naïve Bayes, Random Forest models were also trained and tested individually.

Cab for Heart Diagnosis with RFO Artificial Intelligence Algorithm

CAB coronary artery Blockage is a main difficulty; this causes the heart problems. Different models are utilized to diagnosis the CAB as well as a category of heart problems. This work involves the heart surgery operations & very fast diagnosis. This research just requires the heart images i.e., CT-angiography images. Speed and real diagnosis are possible with technical Image processing (TIP) with the use of ML (Machine Learning) algorithm. With the help of RFO-DT (random forest optimization decision Trees) based, TIP and ML are used to detect the ROH (region of a Heart problem). Entire work consists of 2 stages; at first pre-processing is performed and the second stage DT is extracted, probability values are calculated performed the RFO-DT-ML model. Coronary artery is the main tissue in the heart, so it needs more concentration; normal scanning procedures are not sufficient, so CTA is necessary. In this, data sets are collated from the IEEE data house website. Conventional methods like GA, DE, and GWO are not efficient for heart functionality assessment for coronary artery disorders findings. If a patient with heart diseases have a problem for fast disease findings. So Fast and accurate disease finding models are required; therefore, this model i.e., RFO with AI, gives the best diagnosis results with accuracy. Finally, the design has been done and progressed by 4.766% OV, OF by using 6.5%, OT by 2.5%. These are efficient results.