Cherise Stanley

Passionate about learning through self-development. Developing lifelong skills in data science, software development, engineering, and mathematics.

Neural Networks: Heart Disease Classification

Project description: The goal is to create a classification model that can accurately predict whether a patient will or won’t have heart disease based on their recorded health metrics.

Source: The dataset was obtained from the Heart Disease Classification - Neural Network Kaggle Notebook data page.

Expected Correlations to the Target Feature

The following statements summarise the correlations that were expected based on common public health information.

• Age negatively correlates (expected that younger particpants would be less likely to have heart disease)
• High blood pressure (trestbps) positively correlates (expected that those with higher blood pressure would be more likely to have heart disease)
• Chest pain (cp) negatively correlates (expected that those with recorded chest pain for typical or atypical angina would be more likely to have heart disease)
• High cholesterol (chol) ositively correlates (expected that those with higher cholesterol would be more likely to have heart disease)

if (isAwesome){
  return true
}

2. Assess assumptions on which statistical inference will be based

if (isAwesome){
  return true
}

3. Support the selection of appropriate statistical tools and techniques

4. Provide a basis for further data collection through surveys or experiments

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See the full Jupyter Notebook.