Cardiovascular Disease Prediction for Early Diagnosis

Cardiovascular diseases are the first cause of lethality in the world today. CVD accounts for 45% of all deaths in Europe, 46 times the number of deaths caused by AIDS, tuberculosis, and malaria combined. There are 2,353 deaths from CVD each day in the U.S., based on 2017 data. 

The General Practitioner (GP) is the first physician to diagnose heart diseases based on chest pain symptoms. An independent study shows that the GP’s diagnosis yields 69% of sensitivity and 89% specificity. The next physician in line for diagnosis is the cardiologist. He/She can perform several tests, including electrocardiogram (ECG), exercise stress tests, X-rays, echocardiogram, blood tests, coronary angiography, radionuclide tests, MRI scans, CT scans. ECG, stress tests, and fluoroscopy are standard exams performed on patients suspected of CVD. 

Some of these test results, combined with patient information and symptoms, constitute a data set from which we intend to extract an early CVD diagnosis using Quaartz. The dataset is composed of 303 patients and includes the following 13 variables: 

1. Resting blood pressure,
2. Fasting blood pressure, 
3. Resting ECG values,
4. Stress test depression induced by exercise,
5. Shape of the stress test segment, 
6. Sex, 
7. Age,
8. Chest pain type,
9. Serum cholesterol,
10. Exercise-induced angina,
11. Number of major vessels blocked,
12. Thalassemia,
13. Maximum heart rate achieved.

So far, the diagnosis of CVD has been mostly dependent on the traditional approaches, using trained professionals’ expertise, particularly cardiologists. 

Quaartz’ Machine Learning platform can help automate, in part, the CVD risk prediction. Thus General Practitioners and Cardiologists can get indications extracted from the patient information and test results.