A population-based estimation of maternal mortality in Lagos State, Nigeria using the indirect sisterhood method.

BACKGROUND: Pregnancy and delivery deaths represent a risk to women, particularly those living in low- and middle-income countries (LMICs). This population-based survey was conducted to provide estimates of the maternal mortality ratio (MMR) in Lagos Nigeria. METHODS: A community-based, cross-sectional study was conducted in mapped Wards and Enumeration Areas (EA) of all Local Government Areas (LGAs) in Lagos, among 9,986 women of reproductive age (15-49 years) from April to August 2022 using a 2-stage cluster sampling technique. A semi-structured, pre-tested questionnaire adapted from nationally representative surveys was administered using REDCap by trained field assistants for data collection on socio-demographics, reproductive health, fertility, and maternal mortality. Data were analysed using SPSS and MMR was estimated using the indirect sisterhood method. Ethical approval was obtained from the Lagos State University Teaching Hospital Health Research and Ethics Committee. RESULTS: Most of the respondents (28.7%) were aged 25-29 years. Out of 546 deceased sisters reported, 120 (22%) died from maternal causes. Sisters of the deceased aged 20-24 reported almost half of the deaths (46.7%) as due to maternal causes, while those aged 45-49 reported the highest number of deceased sisters who died from other causes (90.2%). The total fertility rate (TFR) was calculated as 3.807, the Lifetime Risk (LTR) of maternal death was 0.0196 or 1-in-51, and the MMR was 430 per 100,000 [95% CI: 360-510]. CONCLUSION: Our findings show that the maternal mortality rate for Lagos remains unacceptable and has not changed significantly over time in actual terms. There is need to develop and intensify community-based intervention strategies, programs for private hospitals, monitor MMR trends, identify and contextually address barriers at all levels of maternal care

Healthcare Diagnosis Support System for Detection of Heart Disease in a Patient using Machine Leaming Methods

One of the most considerable investigative areas has remained the applications area of medical advancement. The early warning method for heart  disease (HD) is one of these medical technologies. The goal of a healthcare diagnosis support system (HDSS) is to diagnose HD at an early stage  such that the diagnosis can be streamlined, advanced cases stopped, and care costs can be minimized. A machine learning (ML) HDSS for heart  disease identification is obtainable in this study, and it is capable of obtaining and learning information from each patient's experimental data  automatically. The authors employed a dimensionality reduction technique autoencoder (AE) with three ML classifiers detection of HD. The HD  dataset employed for the HDSS was collected from the National Health Service (NHS) database. The result was evaluated using the confusion matrix  performance measures such as accuracy, specificity, detection rate, Fl score, and precision. The result shows that NB+Autoencoder outperformed  the other two classifiers with an accuracy of 57.2% and 55.4 precision.&nbsp