How to Solve Assignments on Building and Deploying a Stroke Prediction Model Using R

Before diving into the technical steps, it’s important to clarify the assignment’s goals.

Typically, stroke prediction assignments aim to:

1. Explore the dataset – Identify the most important patient and clinical characteristics associated with stroke risk.
2. Build a predictive model – Develop a well-validated model that can classify patients based on their likelihood of experiencing a stroke.
3. Deploy the model – Integrate the model into a workflow that can be used by healthcare organizations for decision-making.

In completing such an assignment, you’re not just coding in R—you’re demonstrating a wide range of skills:

• Predictive modeling
• Applied machine learning
• Feature engineering
• Exploratory data analysis (EDA)
• Statistical analysis and modeling
• Data manipulation and transformation
• Interactive data visualization
• Application deployment

Step 2: Exploring the Dataset (EDA)

Assignments typically start with a dataset containing patient-level information such as age, gender, hypertension, heart disease, smoking status, BMI, and glucose levels. The target variable is often binary: stroke (yes/no).

Key EDA steps in R:

# Load libraries library(tidyverse) library(ggplot2) # Import dataset stroke_data <- read.csv("stroke_dataset.csv") # Overview str(stroke_data) summary(stroke_data) # Check missing values colSums(is.na(stroke_data))

What to focus on:

• Data distribution: Visualize continuous variables like age, BMI, and glucose levels.
• Categorical analysis: Use bar plots for categorical variables such as smoking status and gender.
• Correlation analysis: Explore relationships between predictors and stroke outcomes.
• Handling missing data: Impute or remove missing values. For BMI, mean or median imputation is common.

# Example visualization ggplot(stroke_data, aes(x=age, fill=factor(stroke))) + geom_histogram(binwidth=5, position="dodge") + labs(title="Age Distribution by Stroke Outcome")

EDA is critical because it reveals which features carry the most predictive power. For instance, older age and hypertension are usually stronger predictors of stroke risk.

Step 3: Data Cleansing and Transformation

Raw healthcare data is rarely clean. Assignments will expect you to preprocess the dataset before modeling.

Key tasks include:

1. Handling missing values – For categorical features, replace with "Unknown." For numeric variables, impute using median or predictive imputation methods.
2. Encoding categorical variables – Use one-hot encoding in R with model.matrix() or caret functions.
3. Feature scaling – Standardize continuous features like BMI and glucose levels for models sensitive to scale (e.g., logistic regression, SVM).
4. Balancing classes – Stroke datasets are often imbalanced (far fewer positive cases than negatives). Use methods such as SMOTE (Synthetic Minority Oversampling Technique).

# Example: Balancing data library(DMwR) balanced_data <- SMOTE(stroke ~ ., data = stroke_data, perc.over = 200, perc.under = 150)

Step 4: Feature Engineering

Assignments often test your ability to create new features that enhance model performance.

Examples:

• Age categories: Group age into bins (e.g., <40, 40–60, >60).
• BMI categories: Underweight, Normal, Overweight, Obese.
• Interaction terms: Interaction between hypertension and age.

stroke_data <- stroke_data %>% mutate(age_group = case_when( age < 40 ~ "Young", age >= 40 & age <= 60 ~ "Middle-aged", TRUE ~ "Senior" ))

Feature engineering helps models capture nonlinear relationships, which is especially important in medical datasets.

Step 5: Building a Stroke Prediction Model

Now comes the core task: building a predictive model. In assignments, you’ll often need to compare multiple machine learning methods.

Common models for stroke prediction in R:

1. Logistic Regression – Baseline statistical model.
2. Decision Trees – Easy to interpret.
3. Random Forests – Powerful for structured datasets.
4. Gradient Boosting (XGBoost, LightGBM) – State-of-the-art performance.
5. Support Vector Machines (SVM) – Useful for classification problems with complex boundaries.

Logistic Regression example:

# Logistic regression set.seed(123) model_logit <- glm(stroke ~ age + hypertension + heart_disease + avg_glucose_level + bmi, data=stroke_data, family=binomial) summary(model_logit)

Random Forest example:

library(randomForest) set.seed(123) model_rf <- randomForest(factor(stroke) ~ ., data=stroke_data, ntree=500, mtry=5, importance=TRUE) # Variable importance varImpPlot(model_rf)

Step 6: Model Validation

Assignments typically emphasize validation to ensure your model is reliable.

Steps for validation:

1. Train-test split: Divide the dataset (e.g., 70/30).
2. Cross-validation: Use k-fold cross-validation with caret to assess generalizability.
3. Evaluation metrics:

• Accuracy (not reliable with imbalanced data)
• Precision, Recall, F1-score
• ROC curve and AUC

library(caret) set.seed(123) trainIndex <- createDataPartition(stroke_data$stroke, p=0.7, list=FALSE) train <- stroke_data[trainIndex,] test <- stroke_data[-trainIndex,] model_rf <- randomForest(factor(stroke) ~ ., data=train) pred <- predict(model_rf, newdata=test) confusionMatrix(pred, factor(test$stroke))

Assignments may also ask you to compare models and choose the best based on AUC or F1-score.

Step 7: Interactive Visualization and Reporting

Data visualization strengthens the interpretability of your model. Assignments may ask for dashboards or reports that highlight model performance and feature importance.

Tools in R:

• ggplot2 – Static plots for EDA and results.
• plotly – Interactive plots for exploration.
• shiny – Deploy interactive dashboards where users can test the model on new patient inputs.

library(plotly) plot_roc <- plot_ly(x = c(0, 0.2, 0.4, 0.6, 0.8, 1), y = c(0, 0.6, 0.7, 0.8, 0.9, 1), type = 'scatter', mode = 'lines') %>% layout(title = "ROC Curve")

Step 8: Model Deployment

Deployment is what transforms your model from an academic exercise into a practical clinical tool. Assignments may require you to describe or demonstrate deployment.

Deployment approaches in R:

• Shiny App – Build a web application where clinicians can input patient data and get predictions.
• Plumber API – Turn your model into a REST API that integrates with hospital systems.
• RMarkdown Reports – Automate reporting of model outputs for healthcare decision-makers.

Example with Plumber:

# plumber.R library(plumber) #* @post /predict function(age, hypertension, heart_disease, avg_glucose_level, bmi){ new_data <- data.frame(age=as.numeric(age), hypertension=as.numeric(hypertension), heart_disease=as.numeric(heart_disease), avg_glucose_level=as.numeric(avg_glucose_level), bmi=as.numeric(bmi)) predict(model_rf, new_data, type="response") }

This deployment step demonstrates real-world readiness and aligns with the assignment requirement of enhancing organizational decision-making.

Step 9: Interpretation and Clinical Relevance

A strong assignment doesn’t just end with numbers. You need to interpret the results in a clinical context.

For example:

• If the model shows that age and hypertension are top predictors, explain why these factors matter medically.
• Discuss the limitations: models can’t replace doctors, data quality affects predictions, and biases in training data can mislead.
• Highlight potential organizational benefits: early intervention strategies, improved patient monitoring, and efficient resource allocation.

Step 10: Structuring Your Assignment Report

Finally, assignments are graded not just on technical execution but also on clarity and presentation.

A well-structured report should include:

1. Introduction – Purpose of stroke prediction, assignment goals.
2. Dataset Overview & EDA – Key patterns, missing values, distributions.
3. Data Preprocessing – Cleansing, transformation, feature engineering.
4. Modeling – Methods applied, rationale for selection.
5. Validation – Performance metrics, cross-validation results.
6. Deployment – Shiny/Plumber demonstration, potential clinical use.
7. Discussion – Clinical relevance, organizational benefits, limitations.
8. Conclusion – Summary of findings and next steps.

Conclusion

Solving an assignment on building and deploying a stroke prediction model using R requires you to combine multiple skills: from exploratory data analysis and statistical modeling to machine learning, feature engineering, and deployment strategies. The key is to approach the task systematically—explore the data, clean and transform it, build multiple models, validate their performance, and then demonstrate practical deployment.

By following the steps outlined in this guide, students can deliver a robust, clinically relevant solution that goes beyond mere code to address real-world healthcare challenges.

At Statisticshomeworkhelper.com, we help students master these steps, ensuring they not only complete their assignments successfully but also gain the confidence to apply predictive analytics in professional settings.