Big Data in Health Care
Big data in health care is transforming the way we understand and deliver medical services. By bringing together massive volumes of patient records, clinical notes, medical images, genomic data, and information from wearables, health systems can uncover patterns that improve decision-making and patient outcomes. Advanced analytics and AI make it possible to detect disease trends, predict outbreaks, and personalize treatments based on individual risk factors. At the same time, big data helps hospitals and providers improve efficiency, reduce costs, and enhance patient safety by anticipating complications before they occur. Beyond individual care, policymakers and researchers also rely on health data to design effective public health programs, evaluate interventions, and address disparities, making big data a cornerstone of modern, evidence-based health care.
Techniques such as Hadoop, MapReduce, and Apache Spark are widely used to store, process, and analyze big health data efficiently, enabling rapid insights from massive and complex datasets.
This project implements a full pipeline for mortality prediction using clinical data from the MIMIC database. It covers data preprocessing, feature engineering, and predictive modeling with Logistic Regression, SVM, and Decision Trees, along with cross-validation and custom model development . The code demonstrates how big data techniques can be applied to healthcare analytics to generate insights from complex patient event data.