Heart Failure Prediction Classification: Project Overview

See application: link

• This project uses multiple logistic regression to predict heart failure rate
• Create a tool that predicts the rate of heart failure to help people with heart condition assess and predict their heart condition
• Use Logistic Regression and Random Forest Classifier

Code and Resources Used

• Python Version: 3.7
• Packages: pandas, numpy, matplotlib, seaborn, scikit learn
• Kaggle: https://www.kaggle.com/fedesoriano/heart-failure-prediction

About the data

The data contain the following columns:

• Age: age of the patient [years]
• Sex: sex of the patient [M: Male, F: Female]
• ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
• RestingBP: resting blood pressure [mm Hg]
• Cholesterol: serum cholesterol [mm/dl]
• FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
• RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]
• MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]
• ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
• Oldpeak: oldpeak = ST [Numeric value measured in depression]
• ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
• HeartDisease: output class [1: heart disease, 0: Normal]

Data Cleaning

I needed to clean the data up so that it was usable for our model. I made the followinng changes and created the following variables:

• Replaced 0 values in cholesterol with the median
• Removed rows with 0 value in restingECG
• Transformed categorical data into dummies valuables

EDA

I looked at the distributions of the data and the value counts for the various categorical variables. Below are a few highlights from the pivot tables.

Model Building

First I transformed the categorical variables into dummy variables. I also split the data into train and test sets with a test size of 30% I tired two different models and evaluated them using Classification Report (Precision, Recall, f1 score, Accuracy Score). I tried two different models:

• Logistic Regression - Baseline for the model
• Random Forest - Because of the sparse data from the many categorical variables. I thought a normalized regression like rfc would be effective

Model Performance

The Logistic Regression model outperformed the other apprach slightly on the test and validation sets

• Logistic Regression: Accuracy score = 0.87, precision = 0.93, recall = 0.88, f1 score = 0.90
• Random Forest: Accuracy score = 0.88, precision = 0.90, recall = 0.89, f1 score = 0.89