CardioSense Start Assessment

Know Your Heart. Understand Your Health.

Explore your cardiovascular health through commonly measured heart health parameters and receive a personalized model-based assessment.

Start Assessment Learn How It Works
13 Clinical Parameters
01 Assessment Model
Fast Response

Heart Health Assessment

Enter the requested health parameters to receive an automated model-based assessment.

Personal Information

In years

Vital Measurements

Systolic, in mm Hg

Highest heart rate achieved during exercise, in bpm

Laboratory Measurements

Serum cholesterol, in mg/dl

Cardiac Indicators

ST depression induced by exercise, relative to rest

Slope of the peak exercise ST segment

Number of major vessels (0–3) colored by fluoroscopy

Thalassemia test result

How It Works

The assessment follows the same machine learning pipeline used during model training.

  1. 01

    Enter Health Parameters

    The system collects 13 cardiovascular parameters such as age, blood pressure, cholesterol, chest pain type, maximum heart rate and other cardiac indicators.

  2. 02

    Scale & Predict

    The submitted values are passed through the trained StandardScaler and then given to the trained Support Vector Machine model for classification.

  3. 03

    Generate Result

    The SVM uses predict() to determine the class and predict_proba() to calculate the model-estimated probability returned by the FastAPI backend.

About CardioSense

CardioSense is a machine learning application built to classify heart disease likelihood from standard cardiovascular measurements. It demonstrates an applied classification workflow, from data preprocessing through model inference.

Frontend
HTML / CSS / JavaScript
Backend
FastAPI
Machine Learning
Scikit-learn
Model
Support Vector Machine (SVM)
Preprocessing
StandardScaler