Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.

A Deep-Learning Approach to Driver Drowsiness Detection

Intelligent Directional Survey Data Analysis to Improve Directional Data Acquisition

Hepatitis C is a viral illness that primarily impacts the liver, inducing persistent inflammation and potentially resulting in significant liver harm over an extended duration. Its transmission occurs through contact with contaminated blood, and it can be efficiently managed using antiviral medications. Since Hepatitis C is very harmful disorder, this research aims as studying and analyzing it utilizing the Machine learning mechanisms. The online obtainable UCI dataset was taken in this research and trained for predicting five outcomes, which are encoded as 0,1,2,3 and 4. These being a blood donor, Hepatitis, Fibrosis, Cirrhosis and suspect blood donor. The model which is trained using the Random-Forest (RF) algorithm obtains a good performance metrics with an accuracy percentage of 93.5%. In short, this model is an impactful classifier that classifies 5 outcomes by training on the dataset.

The Multi-Class Detection of Five Stages of Hepatitis C Using the Machine Learning Based Random Forest Algorithm

Multiple Sclerosis (MS) is characterized by complex and heterogeneous nature and as a result, there’s currently no cure. Medications can help control the progression and ease the symptoms of MS. The scientific interest in the field of explainable artificial intelligence (AI) comes to the surface and aims to assist computer-aided diagnostic systems to be established in medical use by providing understandable and transparent information to the experts. The objective of this study was to present different learning methods of explainable AI models in the assessment of MS disease based on clinical data and brain magnetic resonance imaging (MRI) lesion texture features and compare them by focusing on the main findings. The learning methods used machine learning and argumentation theory to differentiate subjects with relapsing-remitting MS (RRMS) from progressive MS (PMS) subjects and provide explanations. The results showed that the different learning methods achieved a high accuracy of 99% and gave similar explanations as they extracted the same set of rules. It is hoped that the proposed methodology could lead to personalized treatment in the management of MS disease. 

A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis

Deep learning and classic machine learning models in the automatic diagnosis of multiple sclerosis using retinal vessels

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.

/pdf/comprehensible-machine-learning-based-models-for-the-pre-12w50va6.pdf

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features.

/pdf/explainable-ensemble-based-machine-learning-models-for-3b1im4ul.pdf

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Early detection and classification of a cancer type can aid the patient's subsequent clinical therapy. Cervical cancer is a prevalent type of cancer that affects a woman's cervix because of abnormal cell growth. It is the fourth most common disease in women globally, and early detection has the potential to save lives. In spite of the fact that previous studies achieved outstanding results, their models were highly computationally complex due to the significant number of features incorporated. Therefore, this study is intended to use clinical data to diagnose cervical cancer in its early stages and investigate the effect of Recursive Feature Elimination (RFE) in reducing the number of attributes. The models were trained and tested using a dataset obtained from the Machine Learning Repository at the University of California at Irvine (UCI). The models included Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The findings showed that XGBoost and AdaBoost performed the best, with an accuracy rate of 99.79%, recall rate of 100%, and precision rate of 99.6%, using only 8 features.

Ensemble-Based Machine Learning Models for the Preemptive Diagnosis of Cervical Cancer using Clinical Data


 
 
 Type of funding sources: None.
 
 
 
 Rheumatic mitral valve stenosis (MS) causes progressive left atrial (LA) fibrosis and functional impairment, pulmonary artery systolic hypertension and right ventricular (RV) dysfunction.
 
 
 
 The aim of the study was the evaluate the different characteristics between high gradients (HG) and low gradient (LG) MS and, within the LG group those with high (LG-HF) or low flow (LG-LF). Moreover, we wanted to evaluate if there was any difference in overall complications and atrial fibrillation (AF) incidence.
 
 
 
 A total of 147 patients with isolated severe MS were retrospectively enrolled from the institutional echo database.  Echocardiograms were read offline and clinical information were obtained from patients` records. Patients were divided into a group with HG (31 patients, mean MS gradient > 10 mmHg) and LG (116 patients, mean MS gradient ≤ 10 mmHg). The group of LG was then divided in low flow (Stroke volume index (SVi) ≤ 35 ml/m2, LF-LG: 68 patients) and in high flow (SVi > 35 ml/m2, HF-LG: 48 patients).
 
 
 
 The HG group was younger, although the difference did not reach statistical significance, higher percent of males (45% M vs 22% F, p = 0.02), higher heart rate (HR), left atrial volume, higher tricuspid regurgitation velocity (TVR) and derived pulmonary artery systolic pressure (PASP), RV fractional area changes (RV FAC) but similar RV longitudinal contraction. Moreover, the HG group had more severe MS calculated according to PHT.  The group of LG was divided according to SVi:  LF-LG group compare to HG-MS had higher percent of females (45% F vs 13% M, p < 0.001), similar AF (43% vs 32%, p = ns) and pre surgical complications (19% vs 16%, p = ns). LF-LG group had smaller LA systolic volume (117 ± 51 51 ml vs 148.6 ± 38 ml, p < 0.0001), lower LV-S` (5.3 ± 1.4 vs 6 ± 1.6 cm/s, p = 0.004), lower TRV (2.47 ± 0.5 vs 3.24 ± 0.7 m/s, p < 0.0001), higher TAPSE/PASP (0.64 ± 0.3  vs 0.45 ± 0.29, p < 0.001), lower RV S` (9.79 ± 2.01m/s vs 11.25 ± 3.1 m/s, p = 0.01), higher RVFAC (38.5 ± 9.6% vs 30.9 ± 9.2%, p = 0.0005). The group of LG-LF had also less severe MS (1.3 ± 0.6 cm2 vs 1.15 ± 0.3 cm2, p < 0.01).
 
 
 
 Patients with HG MS were those with worst left ventricular involvement, higher PASP and lower RV function. The LF-LG MS were older, and more females were included and even though they had smaller LA, lower values of PASP they had significantly higher percent of AF and similar pre surgical complications.  It could be speculated that LF-LG severe MS patients could be a different phenotype of the disease.


Severe isolated mitral stenosis: are we facing different phenotypes?

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Papers

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Ensemble-Based Machine Learning Models for the Preemptive Diagnosis of Cervical Cancer using Clinical Data

Severe isolated mitral stenosis: are we facing different phenotypes?