Zombie dan Diversifikasi Pada Masa Covid-19

The financial crisis has caused companies to compete to maintain their financial performance in order to avoid entering the category of "zombie companies." A zombie company is one that has low profits and has experienced losses for several consecutive years. Diversification strategies, such as varying products and/or selling products abroad, are believed to improve a company’s financial performance. This study examines the effect of diversification on zombie companies during the COVID-19 pandemic. The population of this study consists of manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2021. The sampling technique is purposive, with a sample size of 126 companies. This study uses logistic regression analysis to examine the effect of diversification on zombie companies with tangibility, age, and company size as control variables. The results of this study found that diversification has no effect on zombie companies. As for the control variables of tangibility, age, and company size, they have no effect on zombie companies. This study concludes that both product and market diversification do not help companies avoid zombie conditions during the COVID-19 pandemic. This could be due to the global nature of the COVID-19 pandemic, which hinders the export process in all countries that are mostly affected by the pandemic. Even though product diversification has been carried out, the products offered are still related to products whose sales are still affected by the COVID-19 pandemic

Multi Disease Prediction Using HDO Machine Learning Approach

Several machine learning approaches can do predictive analytics on vast volumes of information in various sectors. Predictive analytics in health care is a challenging task. Still, it may ultimately aid physicians in making timely judgments about the health and handling of patients based on vast amounts of information. Breast cancer, diabetes, and heart-related disorders cause numerous fatalities worldwide, yet most of these decreases are attributable to an absence of appropriate screenings. The lack of remedial substructure and a short doctor-to-population proportion contribute to the issue above. Following WHO recommendations, physicians' ratio to affected persons should be in some range; India's doctor-to-public proportion indicates a doctor scarcity. Heart, cancer, and diabetes-related disorders pose a significant danger to humanity if not detected initially. Thus, early detection and identification of these disorders may save many lives. Using classification methods based on machine learning, the focus of this effort is to anticipate dangerous illnesses. Diabetes, heart disease, and breast cancer are discussed in this study. To make this effort easy and accessible to the general community, a web application for therapeutic tests has been developed that use machine learning to create illness predictions. In this study, a web application is created for illness prediction that employs the notion of machine learning-based forecasts for illnesses such as breast cancer, diabetes, and cardiovascular sickness

Diagnostic Value of Non-stress Test Interpreted by Smart Interpretive Software

Background and aim: Using appropriate methods for the assessment of fetal health including non-stress test during high-risk pregnancies due to possible placental insufficiency is of paramount importance. Due to complexity in medical decisions, using information systems is being increased to support complex medical decisions. We conducted this study to measure the diagnostic value of non-stress test interpreted by smart interpretive software. Materials and Methods: This study was carried out on 400 non-stress tests obtained from patients’ records regardless of the results of tests in Bent-Ul-Hoda Hospital, Bojnord, Iran. Then, to increase the accuracy of tests, they were interpreted by two specialists with Master’s degree in Midwifery. Finally, the tests were interpreted by the given software. The diagnostic test accuracy was measured using sensitivity and specificity of the software. Results: Out of 400 selected tests, experts interpreted 126 tests with reaction and 274 cases without reaction. The diagnostic accuracy, sensitivity, and specificity of the software were 92.45%, 94.07, and 88.40, respectively. Conclusion: According to the results, the use of this software for interpreting non-stress test results, reduce false- positive and false-negative diagnoses

Machine Learning Model for Evaluative Performance of Medical Images Using Classifiers

Computer Aided Diagnosis is becoming popular in medical sciences as it provides accuracy and timeliness, the two major aims of medical field. In the work presented here, an algorithm is developed which aims to design an auto-CAD system for the diagnosis of retina abnormalities. Diabetic Retinopathy becomes severe if not diagnosed and treated at the first stage. Age-related Macular Degeneration is another vision threatening disease that occurs in the elderly population and needs serious medical attention. In this research work, these two diseases are considered and the signs of these two diseases are analyzed. A combined database is formed by collecting the images from several standard datasets. The algorithm presented in this work is developed with the combination of two steps, namely, image processing and machine learning. Several image processing algorithms for segmentation and morphological operations are used for the detection of the abnormalities caused by the above mentioned diseases. A set of significant features are selected and evaluated on the abnormalities extracted in the image processing stage. The classification of the abnormalities with a training and a test set is performed using different machine learning algorithms. The random forest classifier is best suited to the dataset used in this research for its performance accuracy and robustness with respect to noise. With the aim of forming a Case Based Reasoning model, we have developed a method of machine learning based classification of different abnormalitie

Mortality Prediction of ICU Cardiovascular Patient: Time-Series Analysis

It is estimated that millions of deaths occur annually, which can be prevented when early diagnosis and correct treatment are provided in the intensive care unit (ICU). In addition to monitoring and treating patients, the physician of the ICU has the task of predicting the outcome of patients and identifying them. They are also responsible for the separation of patients who use special ICUs. Because not necessarily all patients hospitalized in ICU benefit from this unit, and hospitalization in a few cases will only lead to an easier death. Therefore, developing an intelligent method that can help doctors predict the condition of patients in the ICU is very useful. This paper aims to predict the mortality of cardiovascular patients hospitalized in the ICU using cardiac signals. In the proposed method, the condition of patients is predicted 30 minutes before death using various features extracted from the electrocardiogram (ECG) and heart rate variability (HRV) signals and intelligent methods. The paper's results showed that combining morphological, linear, and nonlinear features can predict the mortality of patients with accuracy and sensitivity of 96.7±6.7% and 94.1±5.8%, respectively. As a result, accurate classification of diseases and correct prediction of patients by reducing unnecessary monitoring can help optimize ICU beds' use. According to new and advanced techniques and technologies, it is possible to predict and treat many diseases in ICU, leading to longer patient survival

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets