DeepNeural/heart_disease_classifier_model_v1

Model Description

The following model is designed to predict, given a certain number of inputs, whether a person has and/or is it at risk of acquiring heart disease. This model is composed of 13 input features, and is designed to work within form-based applications, i.e. software applications which require user input.

NOTE: The following model is meant as an assistive tool, and must NOT directly be used to produce the final verdict on a person or patient's condition. As it is meant to promote further evaluations upon having completed its prediction.

Developed by: DeepNeural
Model type: Tabular Classifier
Language(s): English
License: MIT

Model Inputs

Variable Name	Type	Description & Input Value
age	Integer	Patient's age
sex	Binary	Patient's sex (1 = male 0 = female)
chest pain type	Integer	1 = Typical angina, 2 = atypical angina 3 = non-anginal pain 4 = asymptomatic
resting blood pressure	Integer	resting blood pressure (in mm Hg on admission to the hospital)
serum cholestoral in mg/dl	Integer
fasting blood sugar > 120 mg/dl	Binary	is the patient's blood sugar level greater than 120 mg/dl?
resting electrocardiographic results (values 0,1,2)	Integer	0 = normal 1 = having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria
maximum heart rate achieved	Integer
exercise induced angina	Binary	Does the patient suffer from exercise induced angina?
oldpeak	Integer	ST depression induced by exercise relative to rest
the slope of the peak exercise ST segment	Integer	1 = upsloping 2 = flat 3 = downsloping
number of major vessels (0-3) colored by flourosopy	Integer
thal	Integer	0 = normal; 1 = fixed defect; 2 = reversable defect

Model Sources

Repository: https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

Uses

This model is primarily designed for Data Scientists, Software Engineers and Machine Learning Engineers who have an interest in developing heart disease software applications, for various healthcare institutions, ranging from hospitals to clinics. Furthermore, this model is also designed for educational purposes within acadamia, whereby diabetic risk-analysis is a priority of the study.

Foreseeable users of the software applications to be developed with this model include: doctors, nurses (with respect to their patients)

Bias, Risks, and Limitations

Please be adviced that our model was trained on a specific dataset for heart disease classification, and although it has an high level of accuracy and precision, there may come certain moments where misclassifications occur.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More research needed for further recommendations. Furthermore, the following model will continously undergo improvements and testing for better results capable of fixing the limitations mentioned in the previous section.

How to Get Started with the Model

To properly make use of this model, please refer to the illustration below, which showcases how this model can be loaded directly into an application. Please note, that, because it was built with the Scikit-Learn Machine Learning library, the model has been saved as a .joblib file. With that in mind, please proceed by copying the following code into your coding environment (Python).

Install Joblib
```
!pip install joblib
```

Load the model Upon Installation

my_model = joblib.load('heart_disease_classifier_model_v1.joblib')

Make predictions (Binary or Probability)

my_model.predict(X_test)

# For probability-based outputs

my_model.predict_proba(X_test)

NOTE: This model requires input data in a 2-Dimensional format (Pandas Series) with the column names, considering the model is to be used in form-based applications.

Metrics

We tested our model by implementing various ML models, namely: logistic regression, Stochastic Gradient Descent, Support Vector Machines, and K-Nearest Neighbor models. After performing hyperparameter tuning we opted to prioritize the K-Nearest Neighbor model for predictive purposes as it showed the best results. The metrics used were accuracy, precision, recall, f1-score and AUC. The results for our model can be seen in the 'Results' section.

Results

Accuracy - 94% Precision - 94% Recall - 94% AUC ROC - 94%