Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Preventive Healthcare Services01:30

Preventive Healthcare Services

1.7K
Preventive healthcare services keep people healthy via frequent check-ups, screening, and counseling. They primarily aid in disease prevention rather than treating an acute or chronic illness. Preventive treatment also keeps individuals productive and energetic, allowing them to work well into their retirement years. Examples of preventive care services include:
1.7K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

428
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
428
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.3K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.3K
Biostatistics: Overview01:20

Biostatistics: Overview

648
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
648
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

823
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
823
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

4.3K
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
4.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A systematic comparison of machine learning models for missing value imputation in household electricity consumption data.

Scientific reports·2026
Same author

Correction: Profiling Chinese children with symptoms of SpLD, ADHD, or ASD: a transdiagnostic and biopsychosocial study.

BMC psychiatry·2026
Same author

Unmanned Aerial Vehicle Surveillance of Rooftop Aedes Breeding Sites Before Dengue Season - Dongguan City, Guangdong Province, China, 2024-2025.

China CDC weekly·2026
Same author

Profiling Chinese children with symptoms of SpLD, ADHD, or ASD: a transdiagnostic and biopsychosocial study.

BMC psychiatry·2026
Same author

Does digital literacy affect farmers' adoption of agricultural social services? An empirical study based on China Land Economic Survey data.

PloS one·2025
Same author

Graph Intention Embedding Neural Network for tag-aware recommendation.

Neural networks : the official journal of the International Neural Network Society·2024
Same journal

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Neural computing & applications·2026
Same journal

Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Neural computing & applications·2026
Same journal

Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Neural computing & applications·2026
Same journal

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same journal

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Neural computing & applications·2025
Same journal

A fairness scale for real-time recidivism forecasts using a national database of convicted offenders.

Neural computing & applications·2025
See all related articles

Related Experiment Video

Updated: Dec 25, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K

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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Data analyticsDisease preventionHealthcareKnowledge discoveryPrevention methodologies

More Related Videos

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.9K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.8K

Related Experiment Videos

Last Updated: Dec 25, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.9K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.8K

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.