CONTEXT
A healthcare client was increasingly concerned about the delayed diagnosis of Type 2 diabetes, which often led to severe complications and higher treatment costs. The objective was to proactively identify high-risk individuals using existing electronic health records (EHRs) and support early intervention.
RESOLUTION
Our data science team, equipped with healthcare domain expertise, collaborated closely with clinicians to develop a predictive analytics pipeline in Python. Key steps included:
- Data preprocessing using pandas, NumPy, and scikit-learn
- Building and tuning classification models such as Logistic Regression and Random Forest
- Evaluating performance with ROC curves and confusion matrices
- Developing interactive dashboards with Plotly and Dash for clinical decision support
RESULT
The resulting diabetes risk prediction model achieved 85% accuracy, effectively flagging high-risk patients. Screening time was reduced by 40%, enabling faster clinical decisions. The system supported timely lifestyle and medical interventions, leading to improved patient outcomes and reduced long-term complications.
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