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Data Cleansing and Preparation, Model development and training

  • Understanding the data sources and selecting the most reliable source for each parameter is a primary challenge.
  • Identifying the optimal definition to predict positive patients accurately is crucial.
  • The imbalance in the dataset, where positive patients are significantly fewer than negative patients, presents a notable hurdle.
  • Data Preparation
  • Handling missing values
  • Handling imbalanced dataset
  • Feature Engineering
  • Model Training
  • Model Evaluation and testing
  • Identifying best model
  • Technologies Used: Postgre SQL and Python
  • Early detection of health criticality stands as a pivotal advantage, offering the opportunity to identify potential medical concerns at their nascent stages, thereby enabling proactive intervention and management strategies before they escalate into more severe conditions, ultimately contributing to improved patient outcomes and healthcare efficacy.
  • The capability to save patients from emergency situations emerges as a significant benefit, as timely detection and intervention based on early warning signs or risk indicators afford healthcare providers the opportunity to preemptively address emergent medical needs, thereby reducing the likelihood of acute crises, minimizing patient distress, and potentially saving lives.

Germany based Pharmaceutical Company

The challenge lies in effectively navigating the complexities of data sources, defining accurate predictors for positive patients, and addressing the imbalance within the dataset, requiring comprehensive strategies and technologies to ensure robust model performance and reliable insights for decision-making.