COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively. Keywords Machine learning • Predictive model • Diabetes mellitus • Diabetes mellitus type 2 • Random forest This article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar".

Background Iron-deficiency anemia (IDA) or iron deficiency (ID) is by far the most common form of disorder affecting the cognitive development, physical growth and school performance of children in developing countries including Nigeria. Objectives In the present study, we aimed to examine whether IDA or ID, or both are associated with oxidative stress or otherwise by assessing the perturbations in oxidative stress markers including malondialdehyde (MDA), catalase (CAT) and superoxide dismutase (SOD). Methods Here, a total of eighty-one IDA, ID, and healthy control subjects of twenty-seven replicates each, were recruited and investigated. Human serum MDA, CAT and SOD levels were quantitatively analyzed using Enzyme-Linked Immunosorbant Assay. Results Mean serum MDA levels of IDA (5.10 ± 2.35 mmol/L) and ID (4.05 ± 1.35 mmol/L) groups were found to perturb significantly ( p < 0.05), being higher than those of control (3.30 ± 0.95 mmol/L) subjects. Similarly, mean serum MDA levels of IDA (5.10 ± 2.35 mmol/L) group was found to be significantly ( p < 0.05) higher when compared with ID (4.05 ± 1.35 mmol/L) subjects. Conversely, mean serum CAT and SOD activities of IDA (8.35 ± 2.21 ng/mL and 340.70 ± 153.65 ng/mL) group were found to differ significantly ( p < 0.05), and those of ID (9.40 ± 1.47 ng/mL and 435.00 ± 144.75 ng/mL) subjects were found to perturb slightly ( p > 0.05), being lower than those of control (10.40 ± 4.31 ng/mL and 482.12 ± 258.37 ng/mL) subjects. Conclusions Taken together, the results of the present study showed that lipid peroxidation was dramatically increased in both IDA and ID subjects in hydroperoxide-superoxide-dependent manner; in contrast, enzymatic antioxidant capacity was drastically decreased in both IDA and ID groups as evidenced by biochemical markers.

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