Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases

The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. By utilising a structured workflow encompassing data collection, preprocessing, model selection and hyperparameter tuning, training, evaluation, and choice of the optimal model, this research addresses the critical need for improved diagnostic tools. The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection that can be readily implemented and adapted in clinical settings.

Estimating Countries with Similar Maternal Mortality Rate using Cluster Analysis and Pairing Countries with Identical MMR

In the evolving world, we require more additionally the young era to flourish and evolve into developed land. Most of the population all around the world are unaware of the complications involved in the routine they follow while they are pregnant and how hospital facilities affect maternal health. Maternal Mortality is the death of a pregnant woman due to intricacies correlated to pregnancy, underlying circumstances exacerbated by the pregnancy or management of these situations. It is crucial to consider the Maternal Mortality Rate (MMR) in diverse locations and determine which human routines and hospital facilities diminish the Maternal Mortality Rate (MMR). This research aims to examine and discover the countries which are keeping more lavish threats of MMR and countries alike in MMR encountered. Data is examined and collected for various countries, data consists of the earlier years' observation. From the perspective of Machine Learning, Unsupervised Machine Learning is implemented to perform Cluster Analysis. Therefore the pairs of countries with similar MMR as well as the extreme opposite pair concerning the MMR are found.