Smart Healthcare Appointment And Disease Prediction System

• Author(s):

  JanarthananV | JananiPriyaV | ShamlinThisha J | Abirami R

• Keywords:

  Machine Learning, Disease Prediction, Healthcare Informatics, Flask Framework, Risk Classification, Web-Based Application, Artificial Intelligence, Appointment Management

• Abstract:

  The Rapid Advancement Of Artificial Intelligence (AI) And Machine Learning (ML) Technologies Has Significantly Transformed Various Domains, Particularly The Healthcare Sector. Early Disease Detection And Timely Medical Consultation Play A Crucial Role In Reducing Mortality Rates, Treatment Costs, And Healthcare Burden. However, Many Individuals Delay Hospital Visits Due To Lack Of Awareness, Limited Accessibility, Long Waiting Times, Or Uncertainty Regarding The Severity Of Symptoms. This Gap Highlights The Need For Intelligent, Accessible, And Integrated Healthcare Assistance Systems. This Paper Presents A Smart Healthcare Disease Prediction And Appointment System Using Machine Learning, A Web-based Application Designed To Provide Preliminary Medical Diagnosis Support And Seamless Doctor Appointment Scheduling Within A Unified Platform. The Proposed System Leverages Supervised Machine Learning Algorithms Trained On Structured Symptom-disease Datasets To Predict Possible Diseases Based On User-input Symptoms And Demographic Attributes Such As Age And Gender. The Prediction Engine Computes Probability Scores For Multiple Disease Classes And Identifies The Most Probable Condition Along With A Confidence Percentage. Additionally, The System Displays The Top Three Predicted Diseases To Enhance User Awareness And Informed Decision-making. To Further Enhance Reliability, The System Incorporates A Risk Classification Mechanism That Categorizes Predicted Diseases Into Low, Medium, And High Risk Levels Based On Model Confidence And Disease Severity. Unlike Traditional Symptom Checker Applications That Provide Only Textual Suggestions, The Proposed Platform Integrates A Complete Appointment Management Module. Users Can View Available Doctors Categorized By Specialization, Book Appointments Directly After Prediction, And Maintain Structured Appointment History Records. The System Is Implemented Using Python, Flask Framework, And A Relational Database For Secure Data Storage And Session Management. The Modular Architecture Ensures Scalability And Extensibility For Future Integration With Hospital Management Systems, Chatbot Assistance, And Mobile Applications. Experimental Evaluation Demonstrates Reliable Prediction Performance And Smooth System Functionality. The Proposed Solution Contributes Toward Enhancing Digital Healthcare Accessibility, Reducing Unnecessary Hospital Visits, And Supporting Early-stage Medical Decision-making Through Intelligent Automation.

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Other Details

• Paper id:

  IJSARTV12I3104623

• Published in:

  Volume: 12 Issue: 3 March 2026

• Publication Date:

  2026-03-02

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