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.

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%.

Trends in hospital admissions and prescribing due to chronic obstructive pulmonary disease and asthma in England and Wales between 1999 and 2020: an ecological study.

OBJECTIVE: To investigate the trends in hospital admissions and medication prescriptions related to asthma and chronic obstructive pulmonary disease (COPD) in England and Wales. METHODS: An ecological study was conducted between April 1999 and April 2020 using data extracted from the hospital episode statistics database in England and the patient episode database for Wales. The Office of National Statistics mid-year population estimates for 1999 through 2020 were collected, and medication prescription data for 2004-2020 were extracted from the prescription cost analysis database. RESULTS: The total annual number of COPD and asthma hospital admissions for various causes increased by 82.2%, from 210,525 in 1999 to 383,652 in 2020, representing a 59.1% increase in hospital admission rate (from 403.77 in 1999 to 642.42 per 100,000 persons in 2020, p < 0.05). Chronic obstructive pulmonary disease with acute lower respiratory infection accounted for 38.7% of hospital admissions. Around 34.7% of all hospital admissions involved patients aged 75 and older. Around 53.8% of all COPD and asthma hospital admissions were attributable to females. The annual number of prescriptions dispensed for COPD and asthma medications increased by 42.2%. CONCLUSIONS: Throughout the study period, hospital admissions due to chronic obstructive pulmonary disease and asthma, as well as medication prescriptions, increased dramatically among all age groups. Hospitalization rates were higher for women. Further observational and epidemiological research is required to identify the factors contributing to increased hospitalization rates.

Preemptive Diagnosis of Alzheimer's Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques.

Alzheimer's Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.

A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data.

Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients' quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.

A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM.

Glucose is the primary source of energy for cells, which are the building blocks of life. It is given to the body by insulin that carries out the metabolic tasks that keep people alive. Glucose level imbalance is a sign of diabetes mellitus (DM), a common type of chronic disease. It leads to long-term complications, such as blindness, kidney failure, and heart disease, having a negative impact on one's quality of life. In Saudi Arabia, a ten-fold increase in diabetic cases has been documented within the last three years. DM is broadly categorized as Type 1 Diabetes (T1DM), Type 2 Diabetes (T2DM), and Pre-diabetes. The diagnosis of the correct type is sometimes ambiguous to medical professionals causing difficulties in managing the illness progression. Intensive efforts have been made to predict T2DM. However, there is a lack of studies focusing on accurately identifying T1DM and Pre-diabetes. Therefore, this study aims to utilize Machine Learning (ML) to distinguish and predict the three types of diabetes based on a Saudi Arabian hospital dataset to control their progression. Four different experiments have been conducted to achieve the highest results, where several algorithms were used, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), Decision Tree (DT), Bagging, and Stacking. In experiments 2, 3, and 4, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. The empirical results demonstrated promising results of the novel Stacking model that combined Bagging K-NN, Bagging DT, and K-NN, with a K-NN meta-classifier attaining an accuracy, weighted recall, weighted precision, and cohen's kappa score of 94.48%, 94.48%, 94.70%, and 0.9172, respectively. Five principal features were identified to significantly affect the model accuracy using the permutation feature importance, namely Education, AntiDiab, Insulin, Nutrition, and Sex.

Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.

The rapid spread of the Covid-19 outbreak led many countries to enforce precautionary measures such as complete lockdowns. These lifestyle-altering measures caused a significant increase in anxiety levels globally. For that reason, decision-makers are in dire need of methods to prevent potential public mental crises. Machine learning has shown its effectiveness in the early prediction of several diseases. Therefore, this study aims to classify two-class and three-class anxiety problems early by utilizing a dataset collected during the Covid-19 pandemic in Saudi Arabia. The data was collected from 3017 participants from all regions of the Kingdom via an online survey containing questions to identify factors influencing anxiety levels, followed by questions from the GAD-7, a screening tool for Generalized Anxiety Disorders. The prediction models were built using the Support Vector Machine classifier for its robust outcomes in medical-related data and the J48 Decision Tree for its interpretability and comprehensibility. Experimental results demonstrated promising results for the early classification of two-class and three-class anxiety problems. As for comparing Support Vector Machine and J48, the Support Vector Machine classifier outperformed the J48 Decision Tree by attaining a classification accuracy of 100%, precision of 1.0, recall of 1.0, and f-measure of 1.0 using 10 features.