Decision Support System for Predicting Survivability of Hepatitis Patients

Background and Objective

Viral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence data.

Methods

To help in accurate binary classification of diagnosis (survivability or mortality) in patients with severe hepatitis, this paper suggests a deep learning-based decision support system (DSS) that makes use of bidirectional long/short-term memory (BiLSTM). Balanced data was utilized to predict hepatitis using the BiLSTM model.

Results

In contrast to previous investigations, the trial results of this suggested model were encouraging: 95.08% accuracy, 94% precision, 93% recall, and a 93% F1-score.

Conclusions

In the field of hepatitis detection, the use of a BiLSTM model for classification is better than current methods by a significant margin in terms of improved accuracy.

Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).

Hearing loss and contributing factors among airport workers in Malaysia

Sensorineural hearing loss is a common and important source of disability among the workers and often caused by occupational noise exposure. Aims of the study were to determine the prevalence and contributing factors of hearing loss among airport workers. A cross-sectional study was carried out at an airport in Malaysia. This study used stratified sampling method that involved 358 workers who were working in 3 different units between November 2008 and March 2009. Data for this study were collected by using questionnaires eliciting sociodemographic, occupational exposure history (previous and present), life-style including smoking habits and health-related data. Otoscopic and pure-tone audiometric tests were conducted for hearing assessment. Noise exposure status was categorize by using a noise logging dosimeter to obtain 8-hour Time-Weighted Average (TWA). Data was analyzed by using SPSS version 12.0.1 and EpiInfo 6.04. The prevalence of hearing loss was 33.5%. Age >40 years old (aOR 4.3, 95%CI 2.2-8.3) is the main risk factors for hearing loss followed by duration of noise exposure >5 years (aOR 2.5, 95%CI 1.4-4.7), smoking (aOR 2.1, 95%CI 1.2-3.4), duration of service >5 years (aOR 2.1, 95%CI 1.1-3.9), exposure to explosion (aOR 6.1, 95%CI 1.3-29.8), exposure to vibration (aOR 2.2, 95%CI 1.1-4.3) and working in engineering unit (aOR 5.9, 95%CI 1.1-30.9). The prevalence rate ratio of hearing loss for nonsmokers aged 40 years old and younger, smokers aged 40 years old and younger, non-smokers older than 40 years old and smokers older than 40 years old was 1.0, 1.7, 2.8 and 4.6 respectively. This result contributes towards better understanding of risk factors for hearing loss, which is relatively common among Malaysian workers.

Lead poisoning in childhood

We report here a case of lead poisoning in a 20 month old girl who presented with acute encephalopathy and status epilepticus. The major clues leading to the diagnosis were the occupational family history and dense lead lines on X-ray of the long bones. She showed evidence of neurological dysfunction in the initial phase, but she improved steadily, regaining her motor power partially and her vision, although some cognitive and language deficits were already evident. She will need long-term neurological assessment and evaluation to ascertain the extent of permanent brain damage.