Page 47 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD)
Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies
Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

A Technological Revolution in Public
Health Education through Disease Detection

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3
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Sahil Chavhan , Sayali Maske , Prof. Anupam Chaube
1,2,3 Department of Science and Technology,
1,2 G H Raisoni Institute of Engineering and Technology, Nagpur, Maharashtra, India
3 G H Raisoni College of Engineering and Management, Nagpur, Maharashtra, India

ABSTRACT Machine learning algorithms that have been trained on large
The integration of advanced technologies in public health datasets gathered from many medical sources form the basis
has initiated a transformative era in disease detection and of the Multiple Disease Prediction System. Numerous
education. Leveraging tools such as artificial intelligence, parameters are covered by these databases, such as genetic
machine learning, wearable devices, and data analytics, predispositions, lab results, environmental impacts, and
modern systems provide accurate diagnostics while demographic data. The system uses algorithms such as
promoting widespread health literacy. This paper reviews random forests, decision trees, and neural networks to find
current advancements, highlights innovative applications, connections and patterns that might be hard for human
and evaluates the challenges of implementing these specialists to find. The system improves its predictions over
technologies in global healthcare. The review explores the time by continuously assimilating fresh data, guaranteeing
ethical, social, and technical implications of utilizing precision and applicability in a medical environment that is
cutting-edge methods to revolutionize public health changing quickly.
education and disease prevention. By combining robust
2. Conceptual Framework
diagnostic tools with tailored educational initiatives, this
2.1. The Importance of Health Education in Disease
paper outlines a framework for enhancing public health
Prevention
outcomes worldwide.
Health education is the cornerstone of disease prevention,
Machine learning algorithms that have been trained on equipping individuals with the knowledge and skills to make
large datasets gathered from many medical sources form informed health decisions. However, traditional education
the basis of the Multiple Disease Prediction System. methods, such as pamphlets, workshops, and public service
Numerous parameters are covered by these databases, such announcements, often lack personalization and fail to engage
as genetic predispositions, lab results, environmental diverse populations effectively. Technology offers an
impacts, and demographic data. The system uses algorithms opportunity to create dynamic, tailored content that reaches
such as random forests, decision trees, and neural networks individuals across socio-economic and geographic barriers,
to find connections and patterns that might be hard for ensuring equitable access to critical health information.
human specialists to find. The system improves its
2.2. Evolution of Disease Detection Technologies
predictions over time by continuously assimilating fresh
Over the past decades, disease detection technologies have
data, guaranteeing precision and applicability in a medical
evolved from basic laboratory tests and manual diagnostics
environment that is changing quickly.
to sophisticated systems powered by artificial intelligence.

KEYWORDS: Public health, disease detection, artificial Early tools relied heavily on pattern recognition and
statistical modeling, while modern approaches utilize deep
intelligence, health education, machine learning, data
learning models capable of processing complex data such as
analytics, wearable technology, healthcare innovation,
genomic sequences, medical imaging, and real-time health
prevention strategies
metrics. These advancements have significantly reduced

diagnostic errors and shortened timeframes, enabling earlier
1. INTRODUCTION interventions and better outcomes.
Public health faces unprecedented challenges due to the
rising incidence of chronic and infectious diseases, 2.3. Integration of Education and Diagnostics
exacerbated by urbanization, climate change, and global The integration of education and diagnostic technologies
mobility. Advances in disease detection technologies have represents a paradigm shift in public health. Platforms that
not only enhanced diagnostic accuracy but also opened new provide real-time diagnostic results alongside educational
pathways for health education. This paper explores how content empower users to understand their health
integrating technological innovations into public health conditions and take informed actions. For example, AI-driven
education fosters better understanding and prevention of apps can analyze health data from wearable devices and
diseases. By combining diagnostic technologies with deliver tailored recommendations, creating a seamless loop
educational tools, it is possible to create a system that between detection and education.
promotes awareness, accessibility, and improved patient
outcomes. The overarching goal is to leverage technology to 3. RELATED WORK
bridge gaps in healthcare delivery and empower individuals Technologies such as convolutional neural networks (CNNs),
natural language processing (NLP), and wearable health
to take proactive roles in their health management.
monitors have revolutionized disease detection. AI-powered
platforms like IBM Watson and Google Health demonstrate

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