Page 323 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Artificial Intelligence in Health Management Data Collection
AI-based health systems, as explored by Nguyen et al. (2022), Data can be collected from various reliable sources
enable personalized interventions by analysing user-specific depending on the specific requirements of the system. One
data. AI algorithms predict potential health risks and provide primary source is wearable devices such as fitness trackers,
recommendations tailored to individual needs. It can build smartwatches, and medical-grade sensors, which can
on these advancements by incorporating machine learning monitor vital parameters like heart rate, blood pressure,
models to offer predictive health analytics. blood oxygen levels, and physical activity in real time.
Integration of IoT in Healthcare Additionally, electronic health records (EHRs) from hospitals
The Internet of Things (IoT) facilitates seamless connectivity or clinics can provide historical patient data, including lab
between devices, enabling data sharing and holistic health test results, medical history, and prescriptions, which are
management. Alotaibi and Mehmood (2020) discuss the role invaluable for analysis and predictions. Publicly available
of IoT in enhancing patient care through interconnected healthcare datasets, such as those from organizations like the
systems. It could leverage IoT to create a comprehensive World Health Organization (WHO) or the Centres for Disease
ecosystem for personalized health management. Control and Prevention (CDC), can also be utilized for
demographic and epidemiological insights. If the system
Behavioural Health and Digital Platforms involves patient-specific monitoring, direct user input
Digital platforms like MyFitnessPal and Headspace have through questionnaires, mobile applications, or online
shown the effectiveness of personalized health plans in portals can be another source of data. Furthermore, IoT-
encouraging behavioural change. Research by Firth et al. enabled medical devices like glucose monitors, ECG
(2019) highlights how mobile applications can support machines, and respiratory trackers can provide continuous
mental health and fitness goals by providing personalized, and detailed health metrics. Ethical considerations, including
evidence-based strategies. data privacy and user consent, must be prioritized while
Data Privacy and Security Concerns collecting and processing this data.
The collection and analysis of personal health data raise Here's a table of common diseases and their symptoms:
concerns about privacy and security. Studies by Zhang et al.
(2023) underline the importance of adopting secure data Disease Symptoms
storage and processing techniques to maintain user trust. It Sneezing, runny or stuffy nose, sore
could integrate blockchain or advanced encryption to Common Cold throat, coughing, mild headache, low-
address these challenges. grade fever
High fever, body aches, chills, fatigue,
Real-World Applications Influenza sore throat, cough, congestion,
Several healthcare initiatives, such as IBM Watson Health (Flu) headache
and Google Health, focus on providing personalized Sore throat, difficulty swallowing, red
solutions. These platforms have laid the groundwork for this Strep Throat or swollen tonsils, white patches on
to build an adaptive system that addresses gaps like tonsils, fever
accessibility, affordability, and engagement. By synthesizing Increased thirst, frequent urination,
insights from wearable technology, AI, IoT, and digital Diabetes fatigue, blurred vision, slow-heading
platforms, it has the potential to redefine personalized health (Type 2) sores
monitoring and management, ensuring improved health Hypertension
outcomes while addressing privacy and security challenges. (High Blood Headaches (sometimes), shortness of
III. PROPOSED WORK Pressure) breath, nosebleeds (in severe cases)
In this phase, the running process to detect diseases and
their kinds, which are provided in Fig. 1, is defined simply. There are numerous diseases that pose significant challenges
The category of detected diseases is a completely to mankind. These can be broadly categorized inti infectious
comprehensive study. The overview of the proposed diseases, chronic diseases, genetic disorders, and mental
framework for different disorder class is proven in figure 1. health conditions.
As you can see seen from the framework, specific datasets 1. Infectious Diseases
had been used for disease detection and classification. These are cause by pathogens such as bacteria, viruses, fungi
Within the first degree of the framework, photos were or parasites and can spread between individuals.
obtained and rescaled at a certain length as photo pre- Ø COVID-19: Caused by the SARS-CoV-2 virus, it became a
processing strategies. At this degree, the pictures had been global pandemic in 2020, affecting millions worldwide.
subjected to normalisation, and the pixel values have been
confined to a positive value range. Ø Tuberculosis (TB): A bacterial infection caused by
Mycobacterium tuberculosis affecting the lungs.
The technique is split into 4 sub-sections: input of symptoms,
information pre-processing, disease detection, and at last the Ø HIV/AIDS: A viral disease caused by the Human
results for the input i. e. health advice. Immunodeficiency Virus that weakens the immune
system.

Ø Malaria: Caused by Plasmodium parasites transmitted
through mosquito bites.
Ø Influenza: Seasonal flu caused by influenza viruses, with
potential for global pandemics.
2. Chronic Diseases
These are long lasting conditions that often require lifelong
Fig.1 The flow of Proposed work management.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 313