Page 140 - 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
Deep Learning in Medical Imaging: Warnings: Precaution against possible complications or
Convolutional Neural Networks (CNNs) are being used to adverse effects. Using advanced algorithms, users can receive
detect anomalies in medical images, more than 90% of the alerts about allergens by reporting symptoms and medical
time in tasks such as tumor detection and neurological history.
disorder classification for processing medical imaging more Provision of food, urges, and even healthcare exercises.
efficiently.
Another key feature is designed to ensure the project
Chatbots and Virtual Assistants: operates with functionality coupled with its safety practices
Using Natural Language Processing (NLP) technology, through allergy alertness and prevention.
chatbots make automatic, interactive conversations with
Thus, the entire health system ensures that users access 24
patients, which guide them through finding symptoms, and
hours a day, seven days a week via a user-friendly digital
then also provide general medical information.
platform.
Triage Automation:
These automated systems involve originally tagging each An efficient system is characterized by minimal response
case with an attribute to sort it according to the service level times, which can go very low in case of the symptom analysis
agreement (SLA) level. process, quality of results interpretation and patient advice
to maintain system use at times under 3 seconds.
However, despite these advances in healthcare technology, Through it, health advice is based on predefined conditions
the integration required for providing these holistic services in the range: illness, exercise, dietary recommendations etc.
is lacking. The current platform aims are to overcome this In total, the system is loaded with approximately 150
gap through the fusion of real-time analysis, personalized symptoms related to 16 types of illnesses.
recommendations, and a modular architecture that can be
scaled across several medical applications. Data Collection:
The platform relies on a comprehensive dataset designed to
address both routine health management and emergency
situation. The dataset is categorized into:
Symptoms: User-provided input are such as fatigue fever or
chest pain.
Precautions: Preventive measures for emergencies and
lifestyle adjustments for daily routines.
Examples: "Avoid strenuous activity during chest pains" ".
Diet Recommendations: Nutritional plans tailored to user
conditions or goals.
Examples: "Low-sodium diet for hypertension, " "High
protein meals for muscle recovery. ".
III. Proposed Work: Workout Routines: Exercises suitable for emergencies (e.g
New model of healthcare is being proposed through the new recovery-focused) and fitness maintenance.
platform. Its primary objectives include:
Examples include "stretching gentle movements for back
The platform utilizes machine learning algorithms trained on pain" and "Extraordinary weightlifting workouts with high
various datasets to identify symptoms and potential intensity. ".
conditions with precision through Real-Time Symptom
Analysis. Alternative medicines that can be bought over the counter.
Automated Triage is responsible for prioritizing cases based The dataset includes:
on their severity and providing swift response to critical Reports of symptoms: Individual user reports mapped to
emergencies. possible conditions.
Personalized Health Recommendations: Comprehensive information on drugs and their applications
Nutritional recommendations: Individual guidance for for various illnesses is available through medication data.
specific health issues. Nutritional recommendations for specific illnesses: A

Treatment recommendations based on common symptoms guideline
for commonly prescribed drugs. Health profile recommendations for exercise schedule.
Exercise regimens: Individualized exercises for optimal
health.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 130