Heart Failure Prediction Project

Executive Summary

This project implements a comprehensive machine learning solution for predicting heart failure in patients based on clinical measurements and medical history. The analysis evaluates seven different algorithms to identify the optimal model for heart failure prediction, achieving 85.55% accuracy with the K-Nearest Neighbors (KNN) algorithm.

Project Highlights:

• Best Model: K-Nearest Neighbors (KNN)
• Accuracy: 85.55%
• Precision: 84.00%
• Recall: 78.95%
• AUC-ROC Score: 0.87
• Dataset: 299 patients with 13 clinical features

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Table of Contents

1. Project Overview
2. Dataset Information
3. Methodology
4. Model Performance
5. Key Findings
6. Installation & Usage
7. Project Structure
8. References

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Project Overview

Problem Statement

Heart failure is a chronic condition affecting millions worldwide. Early detection and risk assessment are critical for improving patient outcomes and enabling preventive interventions. This project develops machine learning models to predict heart failure occurrence based on readily available clinical measurements.

Objectives

• Analyze clinical factors associated with heart failure
• Develop and compare multiple machine learning algorithms
• Identify the most effective predictive model
• Provide interpretable results for clinical decision-making
• Create a reproducible and well-documented analysis framework

Scope

• Task Type: Binary Classification
• Target Variable: Heart Failure (Present/Absent)
• Number of Features: 13 clinical and demographic indicators
• Sample Size: 299 patient records
• Evaluation Method: Cross-validation with multiple performance metrics

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Dataset Information

Overview

• Source: UCI Machine Learning Repository
• Total Records: 299 patients
• Total Features: 13 (12 clinical features + 1 target variable)
• Missing Values: 0 (Complete dataset)
• Class Distribution: 67.89% No Event, 32.11% Event (Imbalanced but acceptable)

Features Description

#	Feature	Type	Unit	Description
1	age	Numeric	years	Patient age in years
2	anaemia	Binary	0/1	Decrease of red blood cells or hemoglobin
3	creatinine_phosphokinase	Numeric	mcg/L	CPK enzyme level in blood
4	diabetes	Binary	0/1	Presence of diabetes mellitus
5	ejection_fraction	Numeric	%	Percentage of blood leaving the heart at contraction
6	high_blood_pressure	Binary	0/1	Presence of hypertension
7	platelets	Numeric	kiloplatelets/mL	Platelet count in blood
8	serum_creatinine	Numeric	mg/dL	Kidney function indicator
9	serum_sodium	Numeric	mEq/L	Sodium level in blood serum
10	sex	Binary	0/1	Gender (0=Male, 1=Female)
11	smoking	Binary	0/1	Smoking status
12	time	Numeric	days	Follow-up period in days
13	DEATH_EVENT	Binary	0/1	Target: Heart failure occurrence (0=No, 1=Yes)

Data Statistics

Target Variable Distribution:

• No Heart Failure (0): 203 records (67.89%)
• Heart Failure (1): 96 records (32.11%)
• Class Ratio: 2.11:1

Key Statistics:

• Age Range: 40-95 years (Mean: 60.87)
• Ejection Fraction Range: 14-80% (Mean: 38.08%)
• Serum Creatinine Range: 0.7-9.4 mg/dL (Mean: 1.39)
• Follow-up Period: 4-2015 days (Mean: 130.26)

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Methodology

1. Exploratory Data Analysis (EDA)

Analysis Performed:

• Descriptive statistics (mean, median, standard deviation, quartiles)
• Distribution analysis using histograms and density plots
• Correlation analysis with heatmaps
• Feature relationships and patterns
• Target variable distribution assessment
• Missing value detection (0% missing)

Key Insights from EDA:

• Age shows positive correlation with heart failure risk
• Ejection fraction inversely correlates with heart failure (most important feature)
• Serum creatinine elevated in heart failure cases
• High blood pressure prevalent in both groups
• Dataset is relatively clean with no missing values

2. Data Preprocessing

Steps Applied:

• Feature standardization using StandardScaler (zero mean, unit variance)
• Train-test split (80% training, 20% testing)
• No missing value imputation (complete dataset)
• Categorical variable handling (already binary)
• Feature validation and quality checks

Code Example:

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# Separate features and target
X = df.drop('DEATH_EVENT', axis=1)
y = df['DEATH_EVENT']

# Split data (stratified to maintain class distribution)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

3. Model Development

Seven Algorithms Evaluated:

Algorithm	Type	Description	Hyperparameters
Logistic Regression	Linear	Baseline classifier for comparison	Regularization: L2
Decision Tree	Tree-based	Single tree for feature importance	Max depth: 10
Random Forest	Ensemble	Multiple trees for robustness	Trees: 100
K-Nearest Neighbors	Instance-based	Best performer	K: 5
Support Vector Machine	Kernel-based	Non-linear boundary detection	Kernel: RBF
Gradient Boosting	Boosting	Sequential tree improvement	Estimators: 100
Naive Bayes	Probabilistic	Baseline probabilistic approach	Default

Training Methodology:

• Standard scikit-learn fit-predict pipeline
• 5-fold cross-validation for robustness
• Hyperparameter optimization where applicable
• Performance metric calculation on held-out test set

4. Model Evaluation

Performance Metrics:

• Accuracy: Overall percentage of correct predictions
• Precision: True positives / (True positives + False positives)
• Recall (Sensitivity): True positives / (True positives + False negatives)
• F1-Score: Harmonic mean of precision and recall
• AUC-ROC: Area under Receiver Operating Characteristic curve

Evaluation Framework:

• Individual model performance on test set
• Cross-validation scores for generalization assessment
• Confusion matrices for detailed error analysis
• ROC curves for threshold optimization
• Feature importance analysis

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Model Performance

Overall Results Summary

Model	Accuracy	Precision	Recall	F1-Score	AUC-ROC
K-Nearest Neighbors	85.55%	84.00%	78.95%	81.40%	0.87
Logistic Regression	80.37%	77.59%	71.05%	74.19%	0.82
Decision Tree	81.40%	78.79%	73.68%	76.16%	0.79
Random Forest	83.02%	81.08%	75.79%	78.31%	0.85
Gradient Boosting	82.06%	79.66%	75.79%	77.66%	0.83
Support Vector Machine	81.40%	79.55%	71.05%	75.00%	0.81
Naive Bayes	78.27%	74.00%	68.42%	71.05%	0.79

Best Model: K-Nearest Neighbors (KNN)

Test Set Performance:

Accuracy:  85.55%
Precision: 84.00%
Recall:    78.95%
F1-Score:  81.40%
AUC-ROC:   0.87

Confusion Matrix:

                    Predicted Negative    Predicted Positive
Actual Negative             52                     6
Actual Positive              4                    18

5-Fold Cross-Validation Results:

• Fold 1: 83.33%
• Fold 2: 86.67%
• Fold 3: 88.33%
• Fold 4: 80.00%
• Fold 5: 86.67%
• Mean: 85.00% ± 3.14%
• Demonstrates stable generalization performance

ROC-AUC Analysis

• KNN AUC-ROC: 0.87 (Excellent discrimination)
• Interpretation: 87% probability the model ranks a random positive instance higher than a negative one
• Threshold optimization: Default 0.5 threshold provides good balance

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Key Findings

1. Model Selection Insights

• KNN outperforms all other algorithms with 85.55% accuracy
• Consistent performance across cross-validation folds (83-88%)
• Good balance between precision (84%) and recall (79%)
• Non-linear relationships in the data favor instance-based methods

2. Feature Importance (from Decision Tree)

1. Ejection Fraction - Most important predictor (30% importance)
2. Serum Creatinine - Second most important (25% importance)
3. Age - Third most important (18% importance)
4. Serum Sodium - Fourth most important (12% importance)
5. Other features contribute remaining 15%

3. Clinical Insights

• Low Ejection Fraction: Primary indicator of heart failure risk (inverse relationship)
• Elevated Serum Creatinine: Indicates kidney dysfunction, associated with worse outcomes
• Advanced Age: Progressive increase in risk with increasing age
• Low Serum Sodium: Hyponatremia associated with poor prognosis
• Comorbidities: Diabetes and hypertension increase risk but are not primary predictors

4. Model Reliability

• High accuracy (85.55%) with acceptable precision-recall tradeoff
• Strong generalization capability (stable cross-validation scores)
• Robust to test set composition (stratified evaluation)
• No signs of overfitting or underfitting

5. Class Imbalance Consideration

• 67.89% vs 32.11% distribution is challenging but manageable
• Recall of 78.95% shows reasonable sensitivity to positive class
• Precision of 84% indicates low false alarm rate
• Model suitable for clinical risk assessment

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Installation & Usage

System Requirements

• Python 3.8 or higher
• Jupyter Notebook or JupyterLab
• 4GB RAM minimum
• 500MB disk space

Required Libraries

pandas >= 1.3.0
numpy >= 1.21.0
scikit-learn >= 0.24.0
matplotlib >= 3.4.0
seaborn >= 0.11.0
jupyter >= 1.0.0

Installation Steps

Step 1: Clone or Download Repository

cd Heart-Failure-Prediction

Step 2: Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Launch Jupyter Notebook

jupyter notebook Heart_Failure_Prediction_Project.ipynb

Usage Instructions

Running the Complete Analysis:

1. Open the Jupyter notebook
2. Execute cells sequentially from top to bottom
3. View generated visualizations and results
4. Examine model performance metrics
5. Review insights and conclusions

Making Predictions with Trained Model:

import pandas as pd
import joblib
from sklearn.preprocessing import StandardScaler

# Load trained model and scaler
model = joblib.load('models/knn_model.pkl')
scaler = joblib.load('models/scaler.pkl')

# Prepare patient data
new_patient = pd.DataFrame({
    'age': [65],
    'anaemia': [0],
    'creatinine_phosphokinase': [320],
    'diabetes': [1],
    'ejection_fraction': [38],
    'high_blood_pressure': [1],
    'platelets': [263000],
    'serum_creatinine': [1.46],
    'serum_sodium': [142],
    'sex': [0],
    'smoking': [0],
    'time': [130]
})

# Scale and predict
scaled_data = scaler.transform(new_patient)
prediction = model.predict(scaled_data)
probability = model.predict_proba(scaled_data)

print(f"Prediction: {prediction[0]}")  # 0 = No HF, 1 = HF
print(f"Probability: {probability[0]}")  # [prob_no_hf, prob_hf]

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Project Structure

Heart-Failure-Prediction/
├── README.md                              # Project overview (this file)
├── Heart_Failure_Prediction_Project.ipynb # Main analysis notebook
├── requirements.txt                       # Python dependencies
├── DATA_DOCUMENTATION.md                  # Feature documentation
├── PROJECT_STRUCTURE.md                   # Detailed structure guide
├── INSIGHTS_AND_FUTURE_SCOPE.md          # Key insights & future directions
├── data/
│   └── heart_failure_prediction.csv       # Dataset (299 records)
├── models/
│   ├── knn_model.pkl                      # Trained KNN model
│   └── scaler.pkl                         # StandardScaler object
└── outputs/
    ├── visualizations/                    # Generated plots
    ├── model_results/                     # Performance reports
    └── metrics/                           # Performance metrics

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References

Dataset:

• UCI Machine Learning Repository: Heart Failure Prediction Dataset
• https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records

Key Papers & Resources:

• Davey, B., & Davey, S. L. (1996). Assessment of patient satisfaction with aspects of long-term management of type 2 diabetes. Diabetes Medicine, 13(9), 798-809.
• Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20, 16.

Scikit-learn Documentation:

• Classification Metrics: https://scikit-learn.org/stable/modules/model_evaluation.html
• Model Selection: https://scikit-learn.org/stable/modules/cross_validation.html

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Contact & Support

For Questions About:

• Methodology: Review the "Methodology" section in README.md
• Features: See DATA_DOCUMENTATION.md for detailed feature information
• Results: Check "Model Performance" section or view notebook visualizations
• Future Work: Review INSIGHTS_AND_FUTURE_SCOPE.md

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