Comparative Analysis of Predictive Models for the Likelihood of Infertility in Women Using Supervised Machine Learning Techniques

Infertility is a worldwide problem, affecting 8% – 15% of the couples in their reproductive age. WHO estimates that there are 60 - 80 million infertile couples worldwide with the highest incidence in some regions of Sub-Saharan Africa also infertility rate may reach 50% compared to 20% in Eastern Mediterranean Region and 11% in the developed world. Infertility has caused considerable social, emotional and psychological stress between couples, among families, within the individual concerned and the society at large. Historical data constituting information describing the risk factors of infertility alongside the respective infertility likelihood status of women was collected from Obafemi Awolowo University Teaching Hospital Complex (OAUTHC).  The predictive model was formulated using naïve Bayes’, decision trees and multi-layer perceptron algorithm – supervised machine learning algorithms.  The formulated model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) environment.  The results of the performance evaluation of the machine learning algorithms showed that the C4.5 decision trees and the multi-layer perceptron with an accuracy of 74.4% each outperformed the naïve Bayes’ algorithm.  In addition, the decision trees algorithm recognized variables relevant to predicting infertility and a rule that can be applied on patient risk factor records for infertility likelihood prediction was deduced from the tree structure.  This showed how effective machine learning algorithms can be used in predicting the likelihood of infertility in Nigerian women

A MATHEMATICAL MODEL FOR THE ADOPTION OF INFORMATION AND COMMUNICATION TECHNOLOGY IN SCHOOL LIBRARIES IN NIGERIA

This study focused on the development of a mathematical model required for estimating the number of adopters of ICT devices among libraries located in Nigeria. Data for this study was collected from 121 respondents selected based on a research survey approach using simple random sampling. 9 ICT devices were identified, namely: PCs, printers/fax machines, search engines, e-library systems, bulk SMS services, library management systems, bar/QR code readers, projectors and video conferencing. The results showed that the earliest ICT devices were adopted for use in 1997, such as: PCs, printers/fax machines and search engines. The remaining ICT devices were adopted in 2000, such as: e-library, bulk SMS services, library management system, bar/QR code readers, projectors and video conferencing. Polynomial functions of degree, m was used to formulate the mathematical model for the adoption of each ICT device identified based on the cumulative frequency of yearly adopters

Comparative Analysis of Prognostic Model for Risk Classification of Neonatal Jaundice using Machine Learning Algorithms

This study focused on the development of a prediction model using identified classification factors in order to classify the risk of jaundice in selected neonates. Historical dataset on the distribution of the classification of risk of jaundice among neonates was collected using questionnaires following the identification of associated classification factors of risk of jaundice from medical practitioners. The dataset containing information about the classification factors identified and collected from the neonates were used to formulate predictive model for the classification of risk of jaundice using 2 machine learning algorithm – Naïve Bayes’ classifier and the multi-layer perceptron.The predictive model development using the decision trees algorithm was formulated and simulated using the WEKA software.The predictive model developed using the multi-layer perceptron and Naïve Bayes’ classifier algorithms were compared in order to determine the algorithm with the best performance.The result shows that 10 variables were identified by the medical expert to be necessary in predicting jaundice in neonates for which a dataset containing information of 23 neonates alongside their respective jaundice diagnosis (Low, Moderate and High) was also provided with 22 attributes following the identification of the required variables.The 10-fold cross validation method was used to train the predictive model developed using the machine learning algorithms and the performance of the models evaluated The multi-layer perceptron algorithm proved to be an effective algorithm for predicting the diagnosis of jaundice in Nigerian neonate

An Infusion Model for The Adoption of Social Media in Nigerian Tertiary Institution

This study aims to understand the trend of social media adoption among youths especially undergraduates of Nigerian tertiary institutions. This study used a questionnaire for identifying the various social media platforms. Also, the study formulated a polynomial function for estimating the number of students who will adopt the use of social media platforms based on the number of years after the year of social media adoption. The results of the study show that the social media platform adopted by Nigerian undergraduate students include: Facebook, Twitter, Instagram, SnapChat, WhatsApp, LinkedIn, WeChat, ResearchGate, Academia and Line. The results show that the most commonly used platforms are: Facebook, SnapChat, Twitter and Instagram while the earliest adopted platforms include: Facebook in 2007, Twitter in 2009 including Instagram and WhatsApp in 2010. The results showed that the infusion model for the adoption of social media was formulated, using a polynomial function with the best fit of the cumulative frequency distribution of the number of users each year. He studies concluded that using the polynomial function of social media infusion, the total number of future adopters of social media can be estimated from the number of years from the year of the adoption of the platform

Social Network Infusion Model in Nigerian Tertiary Institutions

This study identified the Social Media that were most commonly used by students of tertiary institutions across south-western Nigeria. Structured questionnaires were used to collect information about the Social Media that were adopted by students of tertiary institutions in Nigeria. The results of the study showed that social media adopted were: Facebook, Twitter, Instagram, SnapChat, WhatsApp, LinkedIn, WeChat, ResearchGate, Academia and Line; the most commonly used social media included: Facebook, SnapChat, Twitter and Instagram by at least 55% of the students while ResearchGate, Line, Academia, WeChat and LinkedIn accessed only when there were notifications. The results also showed that the earliest adopted social media included: Facebook in 2007 with 1 user, Twitter in 2009 with 3 users, Instagram and WhatsApp in 2010 with 1 and 8 users respectively. The impact of social media showed that at least 67% of the students suggested it had good impacts on their productivity and functionality as students. This study concluded that among the identified social media, about 55% of students agreed that Facebook, Twitter, Instagram, SnapChat, LinkedIn, Line and ResearchGate were more presently in use. This study showed that using a polynomial model of degree m, the total number of students of higher institutions adopting social media from the year of adoption can be estimated based on the value of the number of years after social media adoption, n (in years)

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research