COVID-19 is an infectious disease discovered after the outbreak began in Wuhan, China, in December 2019. COVID-19 is still becoming an increasing global threat to public health. The virus has been escalated to many countries across the globe. This paper analyzed and compared the performance of three different supervised machine learning techniques; Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) on COVID-19 dataset. The best level of accuracy between these three algorithms was determined by comparison of some metrics for assessing predictive performance such as accuracy, sensitivity, specificity, F-score, Kappa index, and ROC. From the analysis results, RF was found to be the best algorithm with 100% prediction accuracy in comparison with LDA and SVM with 95.2% and 90.9% respectively. Our analysis shows that out of these three classification models RF predicts COVID-19 patient's survival outcome with the highest accuracy. Chi-square test reveals that all the seven features except sex were significantly correlated with the COVID-19 patient's outcome (P-value < 0.005). Therefore, RF was recommended for COVID-19 patient outcome prediction that will help in early identification of possible sensitive cases for quick provision of quality health care, support and supervision.

No abstract

Groundwater is one of the significant sources of drinking water in the world. To protect groundwater quality for domestic consumption, it is important to undertake a periodic investigation of its quality to improve the healthy living of the ever-increasing population. In this paper, we have analyzed physical and chemical (physicochemical) concentration levels in groundwater samples collected from twenty-eight sampling locations during the raining season in Gwale, Northwest Nigeria. About fifteen physicochemical parameters such as electric conductivity (EC), turbidity (Turb), pH,  temperature (Temp), nitrate (NO3), phosphate (PO4), total dissolved solid (TDS), chloride (Cl), sulphate (SO4), calcium (Ca), magnesium (mg), sodium (Na), total hardness (TH), iron (Fe) and alkalinity (Alk) were analyzed. The concentrations levels of groundwater parameters in each sampling location were compared with the permissible limits of drinking water qualities specified by the Nigerian Industrial Standard (NIS) to determine the suitability of drinking water in the study area. Karl Pearson’s coefficient of correlation was applied to the groundwater dataset to identify the influence of each physicochemical parameter to the groundwater contamination. Also, hierarchical cluster analysis was used to classify the groundwater samples based on contamination density as well as to identify the sources of water contamination. The results from correlation analysis revealed that EC, TDS, Ca, TH, Mg, SO4, Na and Cl were influenced the water contamination in many of the study locations based on the conventional significance levels (1% and 5%). From the results of cluster analysis, three statistically significant groups (cluster 1, cluster 2 and cluster 3) were formed which were defined as lower contaminated areas, moderately contaminated areas and higher contaminated areas, respectively. The contamination levels identified in the three clusters were attributed to anthropogenic and industrial activities in the raining season. Therefore, groundwater from cluster 3 (Salanta, Hauren makaranta, Dandago, Mandawari and Magashi) are found unsuitable for drinking. This study is useful in monitoring groundwater quality and could be applied in any other location.

No abstract

IUPAC Name Thiazol-5-ylmethyl N-[1-benzyl-4-[[2-[[(2isopropylthiazol-4-yl)methyl-methylcarbamoyl]amino]-4-morpholinobutanoyl]amino]-5-phenyl pentyl]carbamate Chemical formula : C 40 H 53 N 7 O 5 S 2.