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%.

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.

Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset.

In recent times, researchers have noticed that chronic diseases have become more common. In the Kingdom of Saudi Arabia, the number of patients with thyroid cancer (TC) has become a concern, necessitating a proactive system that can help cut down the incidence of this disease, where the system can assist in early interventions to prevent or cure the disease. In this paper, we introduce our work developing machine learning-based tools that can serve as early warning systems by detecting TC at very early stages (pre-symptomatic stage). In addition, we aimed at obtaining the greatest possible accuracy while using fewer features. It must be noted that while there have been past efforts to use machine learning in predicting TC, this is the first attempt using a Saudi Arabian dataset as well as targeting diagnosis in the pre-symptomatic stage (pre-emptive diagnosis). The techniques used in this work include random forest (RF), artificial neural network (ANN), support vector machine (SVM), and naïve Bayes (NB), each of which was selected for their unique capabilities. The highest accuracy rate obtained was 90.91% with the RF technique, while SVM, ANN, and NB achieved 84.09%, 88.64%, and 81.82% accuracy, respectively. These levels were obtained by using only seven features out of an available 15. Considering the pattern of the obtained results, it is clear that the RF technique is better and, hence, recommended for this specific problem.

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study.

A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.

Hybrid chemometric approach for estimating the heat of detonation of aromatic energetic compounds.

This work presents an elegant technique for estimating the heat of detonation (HD) of thirty organic energetic compounds by combining support vector regression (SVR) and gravitational search algorithm (GSA). The work shows that numbers of nitrogen and oxygen atoms as well as the compound molar mass are sufficient as descriptors. On the basis of three performance measuring parameters, the hybrid GSA-SVR outperforms Mortimer and Kamlet (1968), Mohammad and Hamid (2004) and Mohammad (2006) models with performance improvement of 93.951%, 86.197%, and 47.104%, respectively. The superior performance demonstrated by the proposed method would be of immense significance in containing the potential damage of the explosives through quick estimation of HD of organic energetic compounds without loss of experimental precision.

Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study.

This paper aims to assist in the prevention of Chronic Kidney Disease (CKD) by utilizing machine learning techniques to diagnose CKD at an early stage. Kidney diseases are disorders that disrupt the normal function of the kidney. As the percentage of patients affected by CKD is significantly increasing, effective prediction procedures should be considered. In this paper, we focus on applying different machine learning classification algorithms to a dataset of 400 patients and 24 attributes related to diagnosis of chronic kidney disease. The classification techniques used in this study include Artificial Neural Network (ANN) and Support Vector Machine (SVM). To perform experiments, all missing values in the dataset were replaced by the mean of the corresponding attributes. Then, the optimized parameters for the Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques were determined by tuning the parameters and performing several experiments. The final models of the two proposed techniques were developed using the best-obtained parameters and features. The empirical results from the experiments indicated that ANN performed better than SVM, with accuracies of 99.75% and 97.75%, respectively, indicating that the outcome of this study is very promising.

RECOMBFLOW: a scientific workflow environment for Intragenomic Gene Conversion analysis in bacterial genomes, including the pathogen Streptococcus pyogenes.

Intragenomic Gene Conversion (IGC) is important in the evolution of bacteria but has only been analysed computationally in a few strains of Escherichia coli. This paper describes a scientific workflow system, called RECOMBFLOW, that automates this complex procedure for the analysis of more than 400 bacterial genomes, with a median analysis time per genome of less than 5 minutes. Results show that IGC varies greatly, both between different species and among multiple genomes of the same species. We analyse for the first time the large variation of IGC in the pathogen Streptococcus pyogenes, and also in non-pathogenic bacteria.

Simulation of genomic recombination and the influence of DNA sequence diversity on cluster formation and detectability of recombination: a scientific workflow method.

The detection of recombination from DNA sequences is relevant to the understanding of evolutionary and molecular genetics. We developed a Recombination Simulation Scientific Workflow System (RSSWS) for simulating recombination and using GENECONV to test the effect of pairwise differences in a diverse population on the detectability of recombination. Decreases in recombination rate owing to pairwise differences resulted in population clusters analogous to sympatric speciation under specific conditions and decreases in detectability of recombination, a phenomenon that we call 'cryptic recombination'. This computational method demonstrated the value of scientific workflow methods for analysing a complex process and data driven problem.