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Taiwo Oluwatosin Omolase: How Data-Driven Risk Stratification Can Support Early Stroke Detection and Prevention

Taiwo Oluwatosin Omolase/LinkedIn

Mar 5, 2026, 14:07

Taiwo Oluwatosin Omolase: How Data-Driven Risk Stratification Can Support Early Stroke Detection and Prevention

Taiwo Oluwatosin Omolase, Data Analyst at Kinplus Technologies, shared on LinkedIn:

”Stroke Risk Analytics Dashboard | Healthcare Data Project

I recently analyzed 5,109 patient records from the Kaggle Stroke Prediction Dataset to explore patterns in stroke occurrence.

Key Findings:

Total Stroke Cases: 249 (4.87%)
Average Stroke Age: 67.7 years
Female patients recorded more stroke cases (141 vs 108 males)
73% of stroke patients had no diagnosed hypertension
Age 60+ consistently showed stronger association with stroke occurrence.

What This Project Reinforced:

Raw counts can be misleading in healthcare analytics.
Risk interpretation requires subgroup rate calculations, not just frequency.
For example, while a large number of stroke cases appeared in older non smokers without hypertension, this may reflect population distribution rather than elevated individual risk.

Tools Used:

Power BI | DAX

As a pharmacist with growing interest in healthcare analytics, I am particularly passionate about how datadriven risk stratification can support early detection and preventive medicine.

I welcome feedback from healthcare professionals and data scientists.”

Taiwo Oluwatosin Omolase: How Data-Driven Risk Stratification Can Support Early Stroke Detection and Prevention