Page 142 - 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
The project is modular and can accommodate a range of features, including telemedicine consultations, predictive analytics,
and advanced diagnostic tools.
IV. Proposed Research Model:
Three fundamental stages make up the system's architecture:
1. Input Phase:
A dynamic web interface is used to input symptoms or daily health goals. Comprehensive analysis can be conducted with the
inclusion of multiple symptoms.
2. Processing Phase:
AI models can identify potential conditions through symptom analysis. How Engine for Recommendation: Based on data, the
system generates individualized output in case of emergencies or fitness.
3. Output Phase:
Tailored recommendations include:
Optimal Diets: Food recommendations tailored to individual needs or targets.
Medications: General or condition-specific advice.
Workouts: Activities that are tailored to emergencies or fitness objectives.. Precautions: Emergency response measures or
health.

Figure 1: Architecture of the Proposed System Table 2: Performance Metrics
Phase Components Expected
Metric Description
Input User Interface, Symptom Database Value
Processing ML Models, Knowledge Base, Triage System Accuracy 90–95 Proportion of correct
Output Diet Plans, Medications, Alerts, Precautions (%) recommendations
Response Average time for
V. Performance Evaluation: <2
Time (s) generating outputs
Through rigorous testing, performance metrics were
User Based on usability and
determined: 4.5/5 (avg.)
Satisfaction effectiveness feedback
Accuracy: VI.
Based on 5,000 case outcomes from the test dataset, it Result Analysis:
The initial examination on a simulated dataset yielded the
predicted health conditions with an accuracy of 93%.
following results:
Response Time: The model's accuracy was 93% when it came to mapping
The typical response time for recommendations was less symptoms to recommendations.
than 3 seconds.
Analysis and output generation typically took 1.8 seconds,
User Satisfaction:
according to the average response time.
According to a survey of 150 users, the overall satisfaction
level was 87%. Diagnosed 95% of input cases correctly using symptom
analysis.

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