Quickly determine if you have a heart disease or not

Alen Jo (current maintainer) and John Luke (former maintainer from v1.0.0)

2.0.0 - March 2024

• Added 3 new metrics: Ejection Fraction, Brain Natriuretic Peptide, and C-Reactive Protein
• New GUI screen (a more personal and open approach to monitoring having heart disease)
• Online accounts to track your metrics and information (ability to create login and sign-in)
• Download Manager to automatically update HeartCheck
• Threading support to ensure smooth stability of the GUI application
• Encryption of data after proper software close
• "Under-the-hood" stability fixes and code refactoring with Python 3.11.0

Install the following modules using the package manager pip and run Python 3.11.0

pip install sci-kit
pip install pandas
pip install kivy
pip install joblib

Due to security concerns, the code found here does not reflect the release version of HeartCheck. Instead, the code here is a trial run of HeartCheck simulated in a normal environment to see the GUI environment and its functionality. The username is "guest" and password is "trial"

Frankly speaking, we were thinking through ideas of unpopular programs or apps that users may desire as convenient tools. Additionally, we wanted to fit the theme of the Axxess 2023 Hackathon at UT Dallas, which implied biomedical or medical tools useful in the field. Therefore, the inspiration for the program was to let the user enjoy a tool that is somewhat accurate in producing a result that checks if they have heart disease. However, we hope that with enough resources and data, we can make a tool that helps people identify if they have heart disease or not conveniently (this tool does not mean the user should stray away from the doctor as data is not always representative of a medical diagnosis from a professional).

The program attempts to check if the user has heart disease based on the following parameters:

• age in years (must be from 20-90)
• Gender
• Chest pain type (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain; 4 = asymptomatic)
• Resting blood pressure (in mm Hg on admission to the hospital)
• Serum cholesterol in mg/dl
• Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
• Resting electrocardiographic results (0 = normal; 1 = having ST-T; 2 = hypertrophy)
• Maximum heart rate (between 50 to 200 BPM)
• Exercise induced angina (1 = yes; 0 = no)
• ST depression induced by exercise relative to rest
• Slope of the peak exercise ST segment (1 = upsloping; 2 = flat; 3 = downsloping)
• Number of major vessels (0-3) colored by fluoroscopy
• Thalassemia score (1 = normal, 2 = fixed defect, 3 = reversible defect)
• Ejection Fraction (percentage %)
• Brain Natrieuretic Peptide (measured in pg/mL)
• C-Reactive Protein (measured in mg/L)
• Diagnosis of heart disease (angiographic disease status) (0: No heart disease; 1: Mild Heart Disease; 2: Moderate Heart Disease; 3: Severe Heart Disease; 4: Clinical Heart Disease)

We first approached the problem by figuring out what pre-existing data we could work with that is reliable enough to differentiate between a person with heart disease and not distinctly. Then, we needed to partially learn some medical terminology of the different columns that the pre-existing data-frame has. We then made sure to utilize Python Libraries such as pandas (to extract the data into a data frame), sklearn (to train the classifier to predict a binary output: 0 (for no heart disease) and 1 (for heart disease) from the data frame), and lightgbm to most accurately, efficiently, and rapidly predict a binary output.

Some challenges we ran into were fine-tuning the model to increase prediction accuracy, finding open-source and reliable data to work with, and incorporating an algorithm into a user-friendly GUI.

We are proud that we could train the model to be 60% accurate in producing a binary output: 0 (for no heart disease) and 1 (for heart disease). We are also proud that we could get our program to produce a verdict given random test cases (although, in rare cases, it can detect if a person has heart disease).

We learned about a few examples of supervised learning algorithms with some emphasis on tree-based models (Random Forest Classifiers), linear models (Linear regression), and gradient boosting frameworks (Lightgbm). I (Alen) also learned how to make a working GUI using the Kivy framework, REST APIs for configuring download and upload activities, and database frameworks (like SQLite).

• Migrate to a better database solution (given more concurrently active users at approximately >500)
• Fine-tune classifier model (given more data)
• Account for differences in window sizes
• Configure more of the GUI application (create a logout button and create more windows for settings/credits/support)
• More stability (security and program operation) and code refactoring (later revisions of Python)
• Add security for creation of online accounts (detering robot/automated spam) and removal of stale accounts