Skip to content

Latest commit

 

History

History
68 lines (58 loc) · 3.25 KB

README.md

File metadata and controls

68 lines (58 loc) · 3.25 KB

Classification Maternal Health 5 Algorithms ML

Women experience an unfathomable thrill when they bring revival into the world. Several mother deserve to experience that happiness. However, this situation either becomes frightening for many women in this environment. Pregnancy-related infections, excessive bleeding, and high blood pressure account for two-thirds of all maternal deaths. Complications from pregnancy are one of the major cause of death in girls between the ages in teen. Teenage girls are actually more vulnerable to pregnancy risks since their bodies are still developing. Child brides are more likely to experience pregnancy-related complications because they are less likely to obtain adequate medical care while pregnant or give birth in a medical facility. Maternal mortality is still a major issuein many countries, especially developing ones, despite advances in medical research.The World Health Organization (WHO) reports that every day, almost 810 pregnant women and 6,700 babies pass away. Several non-communicable diseases may arise because of epigenetic modifications linked to maternal diet and chemical exposures. Early life environmental pressures, which are referred to as the new discovery origins of health and disease, are assumed to be caused by these epigenetic modifications. influences the risk of chronic illness. The shortage of doctors and nurses, as well as localization, timing, and distance, are a few of the factors that raise the mortality rate of pregnant women and childbirth (Redondi et al., 2013). According to an estimate by the WHO, 800 women will die every day in 2020 as a result of inadequate resources and treatment (Castillejo et al., 2013). It is challenging to assure both the mother and the unborn child's safety during pregnancy because, despite recenttechnical advancements, the rate of maternal deaths is declining. In this situation, pregnancy- related risks can be decreased by foreseeing difficulties and taking preemptive measures. where it is observed to be greater . It has been discovered that the increased frequency of maternal anemia in LMICs is associated with a number of poor consequences for pregnant women and their babies. For instance, it is estimated that 20% of all maternal deaths globally are caused by anemia during pregnancy . Additionally, it is predicted that anemia contributes to 591,000 perinatal fatalities globally, with South Asia and Africa accounting for the majority of these deaths.

Algorithms Classification -

We used dataprep EDA and 5 algorithms Classification

  • Support Vector Machine(RBF)
  • Xgboost
  • Decision Tree
  • Random Forest
  • Gaussian Naive Bayes

Data Set Information:

Data has been collected from different hospitals, community clinics, maternal health cares through the IoT based risk monitoring system.

  • Age: Age in years when a woman is pregnant.
  • SystolicBP: Upper value of Blood Pressure in mmHg, another significant attribute during pregnancy.
  • DiastolicBP: Lower value of Blood Pressure in mmHg, another significant attribute during pregnancy.
  • BS: Blood glucose levels is in terms of a molar concentration, mmol/L.
  • HeartRate: A normal resting heart rate in beats per minute.
  • Risk Level: Predicted Risk Intensity Level during pregnancy considering the previous attribute.