M.S. Applied Data Science - Capstone Chronicles 2025

2

Class III : Products unlikely to cause adverse health consequences but that violate FDA labeling or manufacturing laws (U.S. FDA, n.d.). A recall event occurs when a company withdraws one or more of its products from the market. Each product involved receives an individual safety rating, referred to as the product classification (Class I, II, or III). The overall Event Classification is determined by the most severe classification assigned to any of the associated products (U.S. FDA, n.d.). For example, if an event involves three products—two classified as Class III and one as Class I—the event classification will be designated as Class I, reflecting the most severe classification among the products. Once individual product classifications are assigned, the event classification is determined accordingly. The time between FDA awareness of the event and final classification varies, but typically occurs within a few days. Timely classification and initiation of recalls are crucial to protect public health. However, delays in this process can lead to prolonged exposure to hazardous products, increasing the risk of adverse health outcomes. For instance, a study analyzing FDA medical device recalls from 2018 to 2022 found that only 26.5% of Class I recalls were terminated within a median of 24 months, indicating prolonged periods during which unsafe devices remained in the market (Darby et al., 2023). The automobile industry is also affected by recalls. A study examining the relationship between recall frequency and innovation by manufacturers found a u-shaped relationship, suggesting that recalls can facilitate innovation, but too many recalls may stifle it (Ni et al., 2023). Other research has shown that lobbying

activities by firms may influence the FDA’s recall classifications in the pharmaceutical industry, raising concerns about the impact on public safety and the push for more objective classification methods (Y. Zhou, 2023). These findings highlight the complexity of the recall process and emphasize the need for improvement through data-driven approaches. The FDA’s ongoing recall database provides valuable information for developing analytical tools to enhance recall management and risk prediction. Given the complexity of product recalls and their implications for public health, industry operations, and regulatory efficiency, there is a need for advanced data science techniques to address several challenges: ●​ Accurate and efficient classification of recall severity ●​ Identification of risk factors and early signs of risk across different product types ●​ Analysis of time-related trends and rising issues in product safety ●​ Establishment of a standardized language and risk assessment for improved communication To address these challenges, this study proposes the development of a multiclass classification and risk prediction system for recalls. This system applies machine learning, natural language processing (NLP), and statistical techniques to create a comprehensive framework for predicting recall severity, identifying risk factors, and providing actionable insights for proactive interventions. The working hypothesis is that by integrating diverse data sources—such as product descriptions, manufacturer information, recall reasons, and historical patterns—a predictive

6