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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

Advancement in Chronic Disease Management: Unveiling
New Frontiers in Diagnosis, Treatment & Prevention

Vedant Golait , Titu Tiwari , Prof. Anupam Chaube
2
1
3
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 Evolution to Hybrid Approaches
The rapid advancement in medical technologies has The transition from single modal to hybrid systems
revolutionized the field of predictive healthcare, enabling represents a significant milestone in medical diagnostics.
the development of sophisticated systems that integrate Traditional ML approaches have been invaluable for tasks
diverse data types to enhance diagnostic accuracy. This such as pattern recognition and predictive modelling, but
paper presents a novel hybrid medical prediction system their scope is often confined to isolated data streams. Hybrid
that synergistically combines deep learning-based image systems, on the other hand, combine the strengths of
analysis and traditional medical data processing techniques traditional ML with the advanced capabilities of deep
to deliver accurate multi-modal diagnostic predictions. learning, enabling the analysis of diverse data types
simultaneously. This synthesis not only enhances diagnostic
The proposed system leverages the strengths of
accuracy but also broadens the spectrum of insights that can
Convolutional Neural Networks (CNNs) and Natural be derived from medical data.
Language Processing (NLP) techniques to synthesize
insights from medical images, structured data, and textual From Single to Multi-Modal
information. Specifically, ResNet-18 is employed for feature Single-modal diagnostic systems often fall short in capturing
extraction from medical images, while term frequency- the full spectrum of patient health, as they analyse either
inverse document frequency (TF-IDF) vectorization is image or textual data in isolation. This limitation can lead to
utilized for processing structured and textual data. fragmented and incomplete diagnostic outcomes. Multi-
modal systems address this gap by integrating various
By integrating CNNs with NLP techniques, the system forms
sources of information, including imaging studies, clinical
a robust architecture capable of identifying complex
notes, and demographic data. By merging these different
relationships between diverse data modalities. This multi-
data types, hybrid systems can construct a more holistic view
modal approach not only enhances diagnostic accuracy but
of a patient's health status, thereby improving diagnostic
also streamlines clinical workflows, offering significant
precision and facilitating better clinical decision-making
potential in predictive healthcare systems.
Need for Integration
The proposed hybrid medical prediction system has far-
Healthcare data is inherently heterogeneous, encompassing
reaching implications for the field of healthcare, enabling unstructured text, structured numeric values, and visual
clinicians to make more informed decisions, improving
information. This diversity poses a significant challenge for
patient outcomes, and reducing healthcare costs. Future
traditional diagnostic systems that are designed to handle
research directions include exploring the application of this
only a single type of data. Multi-modal systems overcome
system to various medical specialties and investigating the
this challenge by offering a unified platform for the
use of other deep learning architectures to further enhance
integration of diverse data types. This approach is crucial for
diagnostic accuracy.
addressing the complexity of modern medical decision-
making and is instrumental in supporting clinicians in high-
KEYWORDS: Natural Language Processing (NLP), CNN, Image stakes environments where accuracy and timeliness are
classification, Text Vectorization paramount.

I. INTRODUCTION Methodology Fusion
In the ever-evolving landscape of medical diagnostics, This paper presents a novel methodology that leverages the
machine learning (ML) techniques like Random Forest and power of Convolutional Neural Networks (CNNs) for image
Support Vector Machines (SVM) have historically played a analysis and Natural Language Processing (NLP) techniques
pivotal role. These systems, while efficient, primarily operate like TF-IDF vectorization for textual data analysis. By
on single-modal data—either image or textual information— combining these methods, we create a robust framework for
thereby limiting their ability to fully comprehend the extracting and synthesizing insights from multi-modal data.
intricate and multifaceted nature of medical data. However, This fusion of techniques not only enhances the diagnostic
recent advancements in deep learning and neural networks process but also lays the groundwork for future innovations
have started a new era of hybrid systems, which seamlessly in medical AI systems.
integrate multiple data modalities to provide a more II. RELATED WORK
comprehensive diagnostic insight. This section describes the related works that have
contributed to the development of predictive models for
chronic diseases. The following literature review provides an

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