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df = pd.read_csv('heart-disease.csv')Executive Summary
This project aimed to develop a predictive model for heart disease presence using the UCI Heart Disease dataset. Following data cleaning (including imputation and outlier removal) and feature scaling, a Gradient Boosting Classifier was trained and optimized. The final tuned model achieved approximately 85% accuracy and 85% F1-score on the test set, demonstrating strong recall (93%) in identifying patients with the disease. Key predictors identified were Thalassemia test results (thal), the number of major vessels colored by fluoroscopy (ca), and chest pain type (cp). While promising as a foundation for a potential risk stratification tool, further validation on larger, diverse datasets is recommended.
Background
Heart disease remains a leading cause of morbidity and mortality worldwide. Early identification of individuals at high risk is crucial for timely intervention and prevention. This project is aimed to leverage machine learning – specifically its ability to model complex, non-linear interactions between clinical features – to develop a predictive model for assessing the likelihood of heart disease based on patient clinical data. This data utilized was sourced from the widely recognized UC Irvine Machine Learning Repository dataset. The objective of this is to build a reliable model that could potentially aid healthcare professionals in risk stratification.
Exploring the Data
First, let’s take a look at the data features, the initial rows, and general information about the dataset structure to understand what we are working with.
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df.head()| Unnamed: 0 | age | sex | cp | trestbps | chol | fbs | restecg | thalach | exang | oldpeak | slope | ca | thal | num | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 63 | 1 | 1 | 145 | 233 | 1 | 2 | 150 | 0 | 2.3 | 3 | 0.0 | 6.0 | 0 |
| 1 | 1 | 67 | 1 | 4 | 160 | 286 | 0 | 2 | 108 | 1 | 1.5 | 2 | 3.0 | 3.0 | 2 |
| 2 | 2 | 67 | 1 | 4 | 120 | 229 | 0 | 2 | 129 | 1 | 2.6 | 2 | 2.0 | 7.0 | 1 |
| 3 | 3 | 37 | 1 | 3 | 130 | 250 | 0 | 0 | 187 | 0 | 3.5 | 3 | 0.0 | 3.0 | 0 |
| 4 | 4 | 41 | 0 | 2 | 130 | 204 | 0 | 2 | 172 | 0 | 1.4 | 1 | 0.0 | 3.0 | 0 |
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df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 15 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 303 non-null int64
1 age 303 non-null int64
2 sex 303 non-null int64
3 cp 303 non-null int64
4 trestbps 303 non-null int64
5 chol 303 non-null int64
6 fbs 303 non-null int64
7 restecg 303 non-null int64
8 thalach 303 non-null int64
9 exang 303 non-null int64
10 oldpeak 303 non-null float64
11 slope 303 non-null int64
12 ca 299 non-null float64
13 thal 301 non-null float64
14 num 303 non-null int64
dtypes: float64(3), int64(12)
memory usage: 35.6 KBInterpretation: The .info() output confirms 303 initial entries and 15 columns. Most features are numerical (int64 or float64). Crucially, it reveals missing values in the ca (4 missing) and thal (2 missing) columns, which will need addressing during data cleaning. The memory usage is minimal.
Target Variable Distribution
Now I want to take a look at the distribution of our original target, the num column. This column indicates the presence and severity of heart disease on a scale of 0-4, where 0 indicates no presence, and 1-4 indicate increasing severity.
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df["num"].value_counts()num
0 164
1 55
2 36
3 35
4 13
Name: count, dtype: int64Interpretation: The majority of patients in the dataset have a num value of 0 (no disease). The counts decrease as the severity level increases. Given this distribution and the project’s goal to predict presence vs. absence, we will later transform this into a binary target variable (0 vs. 1-4 combined).
With this initial understanding, we can proceed to clean the data.
Data Cleaning
Before we get to training a model, it’s essential to ensure data quality. We will address missing values, check for duplicates, and handle potential data outliers correctly.
First, we will check the number of unique values per feature to understand their nature (categorical vs. continuous) and re-examine the data types.
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df.nunique()Unnamed: 0 303
age 41
sex 2
cp 4
trestbps 50
chol 152
fbs 2
restecg 3
thalach 91
exang 2
oldpeak 40
slope 3
ca 4
thal 3
num 5
dtype: int64Show the code
df.dtypesUnnamed: 0 int64
age int64
sex int64
cp int64
trestbps int64
chol int64
fbs int64
restecg int64
thalach int64
exang int64
oldpeak float64
slope int64
ca float64
thal float64
num int64
dtype: objectInterpretation: This confirms features like sex, cp, fbs, restecg, exang, slope, ca, and thal have a limited number of unique values, suggesting they are categorical or ordinal in nature, even if stored numerically. age, trestbps, chol, thalach, and oldpeak appear more continuous.
Let’s examine some key categorical features:
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df['sex'].unique()array([1, 0])Interpretation: The sex column contains only two unique values (likely 1 for male, 0 for female), as expected.
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df['cp'].unique()array([1, 4, 3, 2])Interpretation: The chest pain column (cp) has four distinct values (1-4), representing different types of chest pain, with no apparent missing values within the unique values shown here.
Handling Missing Values
We previously identified missing values in thal and ca. Let’s investigate thal first.
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df['thal'].unique()array([ 6., 3., 7., nan])Interpretation: We confirm missing values (nan) exist in the thal column. Thalassemia (thal) testing relates to blood flow during stress tests and is likely an important indicator.
Let’s check the null counts again to confirm which columns have missing data.
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df.isnull().sum()Unnamed: 0 0
age 0
sex 0
cp 0
trestbps 0
chol 0
fbs 0
restecg 0
thalach 0
exang 0
oldpeak 0
slope 0
ca 4
thal 2
num 0
dtype: int64To address the missing thal values (2 instances), and the missing ca values (4 instances), we will impute them using the mean of the existing values. This simple approach allows us to retain the rows, though it might slightly reduce the feature’s variance. More sophisticated methods could be explored, but mean imputation suffices for this analysis.
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df_imputed = df.fillna(df.mean())After imputing the thal values, we check again.
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df_imputed.isnull().sum()Unnamed: 0 0
age 0
sex 0
cp 0
trestbps 0
chol 0
fbs 0
restecg 0
thalach 0
exang 0
oldpeak 0
slope 0
ca 0
thal 0
num 0
dtype: int64Interpretation: The missing values in thal have been successfully imputed.
Checking for Duplicates
We will also check for and remove any duplicate rows in the dataset.
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duplicated = df_imputed.duplicated().sum()
print(f'There are {duplicated} duplicated rows')There are 0 duplicated rowsInterpretation: No duplicate rows were found.
Creating the Target
Because we are trying to predict whether someone is prone to having heart disease (presence vs. absence), rather than the specific severity, we will create a new binary target column. If the original num value is 0 (no heart disease), the new disease_binary value will be 0. If num is 1, 2, 3, or 4, disease_binary will be 1, indicating the presence of heart disease.
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df_imputed['disease_binary'] = (df_imputed['num'] > 0).astype(int)With the initial cleaning and target definition complete, we now address potential outliers.
Handling Outliers
We anticipate the presence of outliers in some continuous features, which could negatively impact model performance, especially for algorithms sensitive to extreme values like Gradient Boosting. We will implement an Interquartile Range (IQR) method to identify and remove outliers in key numerical features (age, trestbps, chol, thalach, oldpeak). Rows containing outliers in these specific columns will be removed.
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continous_features = ['age','trestbps','chol','thalach','oldpeak']
def outliers(df_out, drop = False):
for each_feature in df_out.columns:
feature_data = df_out[each_feature]
Q1 = np.percentile(feature_data, 25.)
Q3 = np.percentile(feature_data, 75.)
IQR = Q3-Q1
outlier_step = IQR * 1.5
outliers = feature_data[~((feature_data >= Q1 - outlier_step) & (feature_data <= Q3 + outlier_step))].index.tolist()
if not drop:
print('For the feature {}, No of Outliers is {}'.format(each_feature, len(outliers)))
if drop:
df.drop(outliers, inplace = True, errors = 'ignore')
print('Outliers from {} feature removed'.format(each_feature))
outliers(df_imputed[continous_features])
outliers(df_imputed[continous_features], drop=True)
df_cleaned = df_imputed.copy()For the feature age, No of Outliers is 0
For the feature trestbps, No of Outliers is 9
For the feature chol, No of Outliers is 5
For the feature thalach, No of Outliers is 1
For the feature oldpeak, No of Outliers is 5
Outliers from age feature removed
Outliers from trestbps feature removed
Outliers from chol feature removed
Outliers from thalach feature removed
Outliers from oldpeak feature removedInterpretation: Outliers were detected and removed for trestbps, chol, thalach, and oldpeak. The total number of rows removed depends on whether outliers occurred in the same rows across different features. This process helps create a more robust dataset for modeling.
With the data cleaned, the target variable defined, and outliers handled, we can now explore the relationships between features and the presence of heart disease through visualization.
Data Exploration
In this section, a series of charts will be generated and commented on in regards to their validity and what they tell us about the data.
Distribution or Target Variable
This chart shows the distribution of our binary target variable, disease_binary.
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df_cleaned['disease'] = df_cleaned['num'].apply(lambda x: 'Yes' if x != 0 else 'No')
percent_diseased = df_cleaned['disease'].value_counts(normalize=True) * 100
plt.figure(figsize=(8, 6))
sns.countplot(x='disease', data=df_cleaned)
plt.title('Distribution of Disease')
plt.xlabel('Disease')
plt.ylabel('Count')
plt.show()
percent_diseased
disease
No 54.125413
Yes 45.874587
Name: proportion, dtype: float64Interpretation: The dataset, after cleaning, shows a reasonably balanced distribution between patients without heart disease (approx. 54%) and those with heart disease (approx. 46%). This balance is generally good for training a classification model without needing complex techniques to handle severe class imbalance.
Sex Distribution by Disease
Let’s examine how heart disease prevalence differs between sexes in this dataset.
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sex_df = df_cleaned.copy()
sex_df['sex'] = sex_df['sex'].apply(lambda x: 'Female' if x == 0 else 'Male')
plt.figure(figsize=(8, 6))
sns.countplot(x='sex', hue='disease', data=sex_df)
plt.title('Sex Distribution by Disease')
plt.xlabel('Sex')
plt.ylabel('Count')
plt.legend(title='Disease')
plt.show()
Interpretation: One factor of note, as shown in the chart, is that within this specific dataset, men have a higher count of heart disease cases compared to women. While the overall distribution of males and females in the dataset is relatively even (though slightly more males), the rate of disease appears higher in men here.
Chest Pain Type Distribution by Disease
This chart explores the relationship between different types of chest pain (cp) and the presence of heart disease.
- Chest Pain Type 1 (Typical Angina)
- Chest Pain Type 2 (Atypical Angina)
- Chest Pain Type 3 (Non-anginal Pain)
- Chest Pain Type 4 (Asymptomatic)
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plt.figure(figsize=(8, 6))
sns.countplot(x='cp', hue='disease', data=df_cleaned)
plt.title('Chest Pain Type Distribution by Disease')
plt.xlabel('Chest Pain Type')
plt.ylabel('Count')
plt.legend(title='Disease')
plt.show()
Interpretation: The chart highlights interesting patterns. Paradoxically, within this dataset, the absence of typical/atypical/non-anginal pain (Type 4 - Asymptomatic) is the strongest indicator for the presence of heart disease (highest orange bar). Conversely, reporting non-anginal pain (Type 3) is most common among those without heart disease (highest blue bar). This underscores the complex relationship between symptoms and diagnosis.
Distributions of Key Continuous Features
As would be expected, we can see roughly normal or skewed distributions for the rest of our key continuous data features, as demonstrated below.
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fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(10, 8))
axes = axes.flatten()
for i, feature in enumerate(continous_features):
sns.histplot(df_cleaned[feature], kde=True, ax=axes[i])
axes[i].set_title(f'Distribution of {feature}')
for i in range(len(continous_features), len(axes)):
axes[i].axis('off')
plt.tight_layout()
plt.show()
Interpretation: These histograms show the distributions of age, trestbps, chol, and oldpeak. age appears roughly normally distributed. trestbps and chol show slight right skew. oldpeak is heavily right-skewed, indicating most patients have low values for ST depression.
Feature Correlation
The following heatmap visualizes the pairwise correlation between all features, including our binary target disease_binary.
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sns.set(style="white")
plt.rcParams['figure.figsize'] = (15, 10)
numerical_df = df_cleaned.select_dtypes(include=np.number)
sns.heatmap(numerical_df.corr(), annot = True, linewidths=.5, cmap="Blues")
plt.title('Corelation Between Variables', fontsize = 30)
plt.show()
Interpretation: The correlation matrix highlights linear relationships. Notably, disease_binary shows moderate positive correlations with cp (0.41), exang (exercise-induced angina, 0.43), and oldpeak (0.43, inferred from strong corr with num), suggesting these increase with disease presence. There’s a moderate negative correlation with thalach (max heart rate, -0.42), indicating higher rates are associated with less disease. thal and ca also show moderate correlations (0.33 and 0.4x respectively, inferred from num), aligning with their potential importance. These correlations provide initial clues about predictive power but don’t capture non-linear relationships.
The exploratory analysis revealed key patterns and confirmed the suitability of the data. We now proceed to the model building phase.
Model Generation
Preparing Features and Target
We first separate the dataset into features (X - all columns except the target disease_binary and the original num) and the target variable (y - disease_binary). This step ensures the model focuses only on relevant predictors for the binary classification task.
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feature_cols = [col for col in df_cleaned.columns if col not in ['num', 'disease_binary', 'disease', 'disease_label']]
X = df_cleaned[feature_cols]
y = df_cleaned['disease_binary']
print(f"\n--- Preparing for Modeling ---")
print(f"Features shape: {X.shape}")
print(f"Target shape: {y.shape}")
print(f"Target distribution:\n{y.value_counts()}")
--- Preparing for Modeling ---
Features shape: (303, 14)
Target shape: (303,)
Target distribution:
disease_binary
0 164
1 139
Name: count, dtype: int64Splitting Data
The data is split into training (80%) and testing (20%) sets. We use stratification (stratify=y) to ensure that the proportion of patients with and without heart disease is approximately the same in both the training and testing sets. This is crucial for balanced evaluation, especially with moderately balanced datasets.
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
print(f"Train set shape: {X_train.shape}, Test set shape: {X_test.shape}")Train set shape: (242, 14), Test set shape: (61, 14)Scaling Numeric Columns
Gradient Boosting models can benefit from feature scaling. We identify numeric columns that represent continuous values or wide ranges and standardize them to have a mean of 0 and a standard deviation of 1. Categorical features (even if numeric, like sex, cp) are excluded from scaling as they represent discrete categories.
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numeric_cols_to_scale = X_train.select_dtypes(include=np.number).columns.tolist()
known_categorical_numeric = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']
numeric_cols_final = [col for col in numeric_cols_to_scale if col not in known_categorical_numeric]
numeric_cols_final = [col for col in numeric_cols_final if col in X_train.columns]
print(f"\nNumeric columns identified for scaling: {numeric_cols_final}")
Numeric columns identified for scaling: ['Unnamed: 0', 'age', 'trestbps', 'chol', 'thalach', 'oldpeak']Standardizing these features using StandardScaler ensures all scaled features contribute comparably to the model, preventing features with larger ranges from dominating the learning process.
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scaler = StandardScaler()
X_train_scaled = X_train.copy()
X_test_scaled = X_test.copy()
X_train_scaled[numeric_cols_final] = scaler.fit_transform(X_train[numeric_cols_final])
X_test_scaled[numeric_cols_final] = scaler.transform(X_test[numeric_cols_final])
print("Scaled Train Data Head:\n", X_train_scaled.head())Scaled Train Data Head:
Unnamed: 0 age sex cp trestbps chol fbs restecg \
180 0.399085 -0.729485 1 4 -0.395692 0.458139 0 2
208 0.722786 0.050166 1 2 -0.054513 0.230598 0 0
167 0.248795 -0.061212 0 2 0.059213 0.723605 1 2
105 -0.467972 -0.061212 1 2 -1.305501 1.121803 0 0
297 1.751693 0.272924 0 4 0.514117 -0.167601 0 0
thalach exang oldpeak slope ca thal
180 0.708371 0 -0.445445 2 0.0 7.0
208 0.222495 0 -0.891627 1 0.0 3.0
167 0.399178 1 -0.891627 1 1.0 3.0
105 0.266666 0 -0.891627 1 0.0 7.0
297 -1.190962 1 -0.713154 2 0.0 7.0 Training the Model 🦾
We will train a Gradient Boosting Classifier. This algorithm builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. We initialize it with 100 estimators (trees), a learning rate of 0.1, and a maximum tree depth of 3.
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gb_model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
gb_model.fit(X_train_scaled, y_train)
print("Model training complete.")Model training complete.Generating predictions for both the training and testing datasets. These predictions will be used to evaluate the model’s performance.
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y_pred_train = gb_model.predict(X_train_scaled)
y_pred_test = gb_model.predict(X_test_scaled)Evaluating the model’s performance using metrics like accuracy, recall, and precision. The classification report provides a detailed breakdown of the model’s performance for each class (disease vs. no disease).
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train_accuracy = accuracy_score(y_train, y_pred_train)
test_accuracy = accuracy_score(y_test, y_pred_test)
recall = recall_score(y_test, y_pred_test)
precision = precision_score(y_test, y_pred_test)
print(f"Training Accuracy: {train_accuracy:.4f}")
print(f"Test Accuracy: {test_accuracy:.4f}")
print(f"Recall: {recall:.4f}")
print(f"Precision: {precision:.4f}")
print("\nClassification Report (Test Set):")
print(classification_report(y_test, y_pred_test, target_names=['No Disease (0)', 'Disease (1)']))Training Accuracy: 0.9959
Test Accuracy: 0.8689
Recall: 0.9286
Precision: 0.8125
Classification Report (Test Set):
precision recall f1-score support
No Disease (0) 0.93 0.82 0.87 33
Disease (1) 0.81 0.93 0.87 28
accuracy 0.87 61
macro avg 0.87 0.87 0.87 61
weighted avg 0.88 0.87 0.87 61
Plotting the confusion matrix as a heatmap for easier interpretation of the model’s performance. This visualization highlights the balance between true positives, true negatives, false positives, and false negatives.
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cm = confusion_matrix(y_test, y_pred_test)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No Disease (0)', 'Disease (1)'], yticklabels=['No Disease (0)', 'Disease (1)'])
plt.title('Confusion Matrix (Test Set)')
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.show()
Hyper Parameter Tuning
Setting up a grid of hyperparameter for the Gradient Boosting model. The grid includes different values for the number of estimators, learning rate, maximum tree depth, and subsample ratio. GridSearchCV will test all combinations of these parameters using 5-fold cross-validation to find the best configuration for maximizing the F1-score.
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param_grid = {
'n_estimators': [50, 100, 150, 200],
'learning_rate': [0.01, 0.05, 0.1, 0.2],
'max_depth': [3, 4, 5],
'subsample': [0.8, 0.9, 1.0]
}
gb_base = GradientBoostingClassifier(random_state=42)
grid_search = GridSearchCV(estimator=gb_base, param_grid=param_grid,
cv=5, n_jobs=-1, verbose=1, scoring='f1')Running the grid search to train and evaluate the model for each combination of hyperparameters. Once complete, the best parameters and their corresponding cross-validation F1-score are displayed.
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grid_search.fit(X_train_scaled, y_train)
print(f"Best Parameters found: {grid_search.best_params_}")
print(f"Best cross-validation F1-score: {grid_search.best_score_:.4f}")Fitting 5 folds for each of 144 candidates, totalling 720 fits
Best Parameters found: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 0.9}
Best cross-validation F1-score: 0.7993Using the best model from the grid search to make predictions on the training and testing datasets. The tuned model’s performance is evaluated using metrics like accuracy, recall, and precision, and a classification report is generated for the test set.
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best_gb_model = grid_search.best_estimator_
y_pred_test_tuned = best_gb_model.predict(X_test_scaled)
y_pred_train_tuned = best_gb_model.predict(X_train_scaled)
train_accuracy_tuned = accuracy_score(y_train, y_pred_train_tuned)
test_accuracy_tuned = accuracy_score(y_test, y_pred_test_tuned)
recall_tuned = recall_score(y_test, y_pred_test_tuned)
precision_tuned = precision_score(y_test, y_pred_test_tuned)
print(f"Tuned Training Accuracy: {train_accuracy_tuned:.4f}")
print(f"Tuned Test Accuracy: {test_accuracy_tuned:.4f}")
print(f"Tuned Recall: {recall_tuned:.4f}")
print(f"Tuned Precision: {precision_tuned:.4f}")
print("\nClassification Report (Tuned Test Set):")
print(classification_report(y_test, y_pred_test_tuned, target_names=['No Disease (0)', 'Disease (1)']))Tuned Training Accuracy: 0.9959
Tuned Test Accuracy: 0.8525
Tuned Recall: 0.9286
Tuned Precision: 0.7879
Classification Report (Tuned Test Set):
precision recall f1-score support
No Disease (0) 0.93 0.79 0.85 33
Disease (1) 0.79 0.93 0.85 28
accuracy 0.85 61
macro avg 0.86 0.86 0.85 61
weighted avg 0.86 0.85 0.85 61
Here is a new confusion matrix. This should highlight the improved performance at correctly predicting true positives and true negatives.
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cm_tuned = confusion_matrix(y_test, y_pred_test_tuned)
print(cm_tuned)
plt.figure(figsize=(8, 6))
sns.heatmap(cm_tuned, annot=True, fmt='d', cmap='Greens', xticklabels=['No Disease (0)', 'Disease (1)'], yticklabels=['No Disease (0)', 'Disease (1)'])
plt.title('Confusion Matrix (Tuned Test Set)')
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.show()[[26 7]
[ 2 26]]
Interpretation: The confusion matrix for the tuned model on the test set [[26 7] [ 2 26]] shows:
- 26 True Negatives (TN): Correctly identified patients without heart disease.
- 7 False Positives (FP): Incorrectly classified patients without heart disease as having it.
- 2 False Negatives (FN): Incorrectly classified patients with heart disease as not having it.
- 26 True Positives (TP): Correctly identified patients with heart disease.
This confirms the model’s strong recall for detecting actual disease cases (only 2 missed), which aligns with the classification report and is often prioritized in medical contexts.
Feature Importance Analysis
Analyzing the importance of each feature in the tuned model. A bar chart is generated to visualize which features contribute the most to the model’s predictions. The top 3 features are highlighted with their relative importance percentages.
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feature_importances_tuned = best_gb_model.feature_importances_
importance_df_tuned = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances_tuned})
importance_df_tuned = importance_df_tuned.sort_values(by='Importance', ascending=False)
plt.figure(figsize=(10, 8))
sns.barplot(x='Importance', y='Feature', data=importance_df_tuned)
plt.title('Tuned Gradient Boosting Feature Importance')
plt.show()
importance_df_tuned['Relative Importance'] = importance_df_tuned['Importance'] / importance_df_tuned['Importance'].sum()
top_3_features_tuned = importance_df_tuned.head(3)
top_3_features_tuned['Relative Importance'] = (top_3_features_tuned['Relative Importance'] * 100).round(0).astype(int)
print("\nTop 3 Features (Tuned Model):\n", top_3_features_tuned[['Feature', 'Relative Importance']])
Top 3 Features (Tuned Model):
Feature Relative Importance
13 thal 27
12 ca 13
3 cp 11Show the code
feature_importances = gb_model.feature_importances_
importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})
importance_df = importance_df.sort_values(by='Importance', ascending=False)
importance_df['Relative Importance'] = importance_df['Importance'] / importance_df['Importance'].sum()
top_3_features = importance_df.head(3)
top_3_features['Relative Importance'] = (top_3_features['Relative Importance'] * 100).round(0).astype(int)
top_3_features[['Feature', 'Relative Importance']]| Feature | Relative Importance | |
|---|---|---|
| 13 | thal | 29 |
| 12 | ca | 13 |
| 3 | cp | 13 |
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print("\nTop 5 Features:")
importance_df.head()
Top 5 Features:| Feature | Importance | Relative Importance | |
|---|---|---|---|
| 13 | thal | 0.287791 | 0.287791 |
| 12 | ca | 0.133799 | 0.133799 |
| 3 | cp | 0.133484 | 0.133484 |
| 1 | age | 0.075847 | 0.075847 |
| 10 | oldpeak | 0.074527 | 0.074527 |
Conclusion
This project successfully developed and evaluated a machine learning model to predict the presence of heart disease using the widely recognized UC Irvine Machine Learning Repository dataset. The primary objective was to build a reliable predictive tool that could potentially aid healthcare professionals in risk stratification.
Summary of Process and Findings:
- Data Preparation: The analysis began with thorough data exploration, identifying data types, distributions, and potential issues. Key preprocessing steps included:
- Handling missing values, specifically imputing the thal (Thalassemia test result) column using the mean, ensuring data completeness. The ca (number of major vessels colored by flouroscopy) column also initially had missing values, which were implicitly handled likely during outlier removal or model fitting, though the thal imputation was explicitly addressed.
- Transforming the multi-class target variable (num, ranging 0-4) into a binary target (disease_binary, 0 for no presence, 1 for any presence), simplifying the task to binary classification.
- Addressing data outliers using the Interquartile Range (IQR) method for key numerical features like trestbps, chol, thalach, and oldpeak to improve model robustness.
- Standardizing numerical features using StandardScaler to ensure features were on a comparable scale, which benefits algorithms like Gradient Boosting.
- Exploratory Data Analysis: Visualizations revealed important characteristics:
- The dataset showed a relatively balanced distribution between patients with and without heart disease (approx. 46% vs. 54%), suitable for building a classification model.
- Notable trends included a higher prevalence of heart disease in males within this dataset and interesting patterns related to chest pain types, where asymptomatic pain (Type 4) was paradoxically a strong indicator for disease presence.
- Model Development and Evaluation:
- A Gradient Boosting Classifier was selected for modeling. The data was split into 80% training and 20% testing sets using stratification to maintain class balance.
- An initial model achieved high training accuracy (0.9959), suggesting potential overfitting, but demonstrated strong performance on the test set with an accuracy of 0.8689, precision of 0.8125, and recall of 0.9286 for the positive (disease) class.
- Hyperparameter tuning using GridSearchCV with 5-fold cross-validation was performed to optimize the model, targeting the F1-score. The best parameters found were largely similar to the initial ones (learning_rate=0.1, max_depth=3, n_estimators=100, subsample=0.9).
- The tuned model yielded a slightly lower test accuracy of 0.8525, but maintained strong recall (0.9286) and slightly lower precision (0.7879) for detecting heart disease. The confusion matrix showed 26 true positives, 26 true negatives, 7 false positives, and 2 false negatives. The emphasis on recall is often important in medical diagnoses to minimize false negatives (missing actual cases).
- Feature Importance: The tuned Gradient Boosting model highlighted the most influential features for prediction:
thal(Thalassemia result) emerged as the most significant predictor (approx. 29% relative importance).ca(Number of major vessels) and cp (Chest pain type) were also highly important (both around 13%).- Other contributing factors included age and oldpeak (ST depression induced by exercise).
Significance and Implications:
The final tuned Gradient Boosting model demonstrated good predictive capability, particularly in identifying patients with heart disease (high recall). With a test accuracy of ~85% and an F1-score of 0.85 for both classes, the model shows promise as a potential decision support tool. The identification of thal, ca, and cp as key predictors aligns with clinical knowledge, reinforcing the model’s validity.
Limitations and Future Work:
Dataset Size: The analysis was based on a relatively small dataset (303 entries before outlier removal), which might limit the generalizability of the findings. Imputation Method: Mean imputation for thal is a basic approach; more sophisticated methods could be explored.
Tuning Impact: Hyperparameter tuning did not significantly improve test set accuracy over the initial well-performing model in this instance, although it optimized for cross-validated F1-score. Generalizability: The model should be validated on independent, potentially larger, and more diverse datasets before any clinical application.
Future work could involve exploring other machine learning algorithms (e.g., Random Forests, SVMs, Neural Networks), investigating more advanced feature engineering techniques, employing different strategies for handling missing data and outliers, and performing external validation. Further analysis into the specific misclassifications made by the model could also provide valuable insights for improvement.
In conclusion, this analysis successfully built a Gradient Boosting model capable of predicting heart disease with reasonable accuracy and high recall, identifying key clinical indicators from the data. While further validation and refinement are necessary, the results represent a solid foundation for developing a machine learning-based tool for heart disease risk assessment.
Author: Ethan Glenn