Dataset Overview

The dataset utilized in this project originates from a publication by David Chico and Guispe Jurman. Their research explores how machine learning can forecast survival rates of heart failure patients based solely on serum creatinine levels and ejection fraction. It is crucial to address common misunderstandings that beginners might encounter, as neglecting these could lead to significant issues down the line.

Understanding Scikit-Learn

Scikit-learn is an open-source library in Python tailored for machine learning tasks. It encompasses a range of algorithms and tools for data analysis and predictive modeling, including classification, regression, clustering, dimensionality reduction, model selection, and data preprocessing. Built on top of popular libraries like NumPy, SciPy, and Matplotlib, Scikit-learn is designed for seamless integration into machine learning workflows.

Key Features of Scikit-Learn:

• Algorithm Implementation: Efficient implementations of various machine learning algorithms, including support vector machines (SVM), decision trees, and k-means clustering.
• Data Pre-Processing Tools: Facilities for normalizing data, encoding categorical variables, and extracting features.
• Model Evaluation: Metrics for assessing model performance, including precision, recall, F1-score, and cross-validation techniques.
• Model Selection: Tools for automated model selection and hyperparameter tuning.
• User-Friendly API: A consistent and intuitive API that simplifies experimentation with different algorithms.

Scikit-Learn is a favored choice among the Python machine learning community.

Developing Models with Scikit-Learn

In this tutorial, we will explore how to construct several machine learning models, such as linear regression, KNN, decision trees, random forests, and support vector machines, specifically for predicting the mortality of heart patients.

To access the dataset required for developing these models, please follow the link provided.

Using Pandas to Sample Data

We can sample our dataset using the head function in Pandas. It's important to note that understanding Pandas is crucial for the entire project. If you're unfamiliar with it and would like more content about Pandas, please comment below!

# Sample data

dataset.head()

Splitting Data for Training and Testing

When separating the training and test datasets, it’s vital to ensure that no future information is introduced into the training set during preprocessing. If you aim to estimate generalization error, you should use the test set only once!

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(

X, y, test_size=1/3, random_state=seed

)

Validation Set Creation

We will divide the training set into two parts: one for training and another for validation. This helps in determining the most effective model. It’s imperative to only use the test set once to select the best model.

X_train_2, X_val, y_train_2, y_val = train_test_split(

X_train, y_train, test_size=1/3, random_state=seed

)

Feature Scaling

Most algorithms require features to be on a similar scale; hence, scaling is necessary.

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

X_train_2 = scaler.fit_transform(X_train_2)

X_val = scaler.fit_transform(X_val)

Model Evaluation

Next, we will evaluate several models to find the best candidate. For simplicity, we will initially use a basic approach, but we will explore more sophisticated methods in subsequent sections.

from sklearn.metrics import accuracy_score

# Logistic Regression

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression(random_state=seed)

lr.fit(X_train_2, y_train_2)

dic["Logistic_Regression"] = accuracy_score(y_val, lr.predict(X_val))

# KNN

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=7)

knn.fit(X_train_2, y_train_2)

dic["KNN_7"] = accuracy_score(y_val, knn.predict(X_val))

# Decision Tree

from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier(max_depth=4, random_state=seed)

tree.fit(X_train_2, y_train_2)

dic["Decision_Tree_4"] = accuracy_score(y_val, tree.predict(X_val))

# Random Forest

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100, random_state=seed)

rf.fit(X_train_2, y_train_2)

dic["Random_Forest_100"] = accuracy_score(y_val, rf.predict(X_val))

# Linear SVM

from sklearn.svm import LinearSVC

svc = LinearSVC()

svc.fit(X_train_2, y_train_2)

dic['SVC'] = accuracy_score(y_val, svc.predict(X_val))

Evaluating Model Performance

We will summarize model performances in a validation series.

Final Model Training

Once the best model is identified, we can utilize the test set to estimate the generalization error.

scaler = MinMaxScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

# Training the best model

lr = LogisticRegression(random_state=seed)

lr.fit(X_train, y_train)

Conclusion and Next Steps

While there's potential for improving mortality prediction accuracy, I want to congratulate you for reaching this point. If you have any questions, feel free to reach out via email or leave comments below. Let’s keep the discussion about Scikit-Learn models going. Until next time!

Video Resources

To enhance your understanding of predictive modeling in healthcare, check out the following videos:

• Predictive Survival Analysis with Scikit-Learn: This video features Olivier Grisel discussing methods for predicting survival rates using Scikit-Learn, scikit-survival, and lifelines.

• Responsible Implementation of Machine Learning Models: This talk from Eindhoven explores the ethical considerations in deploying machine learning models for life-and-death decisions.