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Big data analytics for preventive medicine
Muhammad Imran Razzak1, Muhammad Imran2, Guandong Xu1
1Advanced Analytics Institute, University of Technology, Sydney, Australia.
Neural Computing & Applications
|March 25, 2020
Summary
Data analytics offers powerful tools for healthcare, improving patient care and reducing costs by analyzing complex medical data. This review explores advanced methods for disease prevention using data analytics, highlighting key algorithms and applications.
Area of Science:
- Health Informatics
- Data Science in Healthcare
- Biostatistics
Background:
- Medical data analysis presents unique challenges due to its complexity, volume, and heterogeneity.
- Traditional disease prevention methods often lack the scalability and predictive power of modern analytics.
- Data analytics offers a promising approach to extract actionable insights from vast healthcare datasets.
Purpose of the Study:
- To provide a comprehensive overview of data analytics methods applied to disease prevention.
- To summarize state-of-the-art algorithms for disease classification, clustering, anomaly detection, and association.
- To discuss recent advancements, applications, and challenges in data-driven disease prevention.
Main Methods:
- Review of existing literature on data analytics in disease prevention.
- Summarization of algorithms including classification, clustering, anomaly detection, and association rule mining.
- Analysis of advantages, drawbacks, and selection guidelines for various data analytics models.
Main Results:
- Data analytics enables efficient pattern discovery in complex healthcare data for forecasting and decision-making.
- Specific algorithms show promise in classifying diseases, identifying disease clusters, detecting anomalies, and finding associations.
- Successful applications demonstrate the potential to improve patient care quality and reduce healthcare costs.
Conclusions:
- Data analytics is a breakthrough technology for enhancing disease prevention strategies.
- Further research is needed to address open challenges and optimize the application of these methods.
- Recommendations are provided for the selection and implementation of data analytics models in healthcare.
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