Health Risk Prediction Model

Requirements Clarification & Assessment

1. Objective Definition:

   • The primary objective is to develop a predictive model that can accurately determine the risk of an individual experiencing significant health issues.
   • Clarify whether the goal is a binary classification (risk/no risk) or a multi-label classification (different types of health risks).

2. Health Issue Definition:

   • Collaborate with healthcare professionals to define "major health issues." This could include conditions like cancer, heart attacks, diabetes, etc.
   • Understand the criteria used to classify a health issue as "major," considering both medical and insurance perspectives.

3. Data Scope and Features:

   • Identify the range of health-related features available, such as medical history, lifestyle factors, genetic predispositions, etc.
   • Determine if demographic data will be used and how it aligns with ethical considerations.

4. Geographical and Market Context:

   • The model is intended for the US market, so ensure data and health risk factors are relevant to this demographic.

5. Business Impact and Constraints:

   • Understand the business implications of false positives and false negatives.
   • Clarify the acceptable trade-offs between sensitivity, specificity, and overall model accuracy.

6. Data Quality and Availability:

   • Assess the completeness of the data and the prevalence of missing values.
   • Determine strategies for handling missing or incomplete data, considering the impact on model performance.