PREDICTION OF THE OCCURRENCE OF STROKE BASED ON MACHINE LEARNING MODELS

2024;
: 17- 27
https://doi.org/10.23939/cds2024.01.017
Received: March 11, 2024
Revised: April 01, 2024
Accepted: April 05, 2024
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University

The research conducted in the medical domain addressed a topic of significant importance, steadily growing in relevance each year. The study focused on predicting the onset of strokes, a condition posing a grave risk to individuals' health and lives. Utilizing a highly imbalanced dataset posed a challenge in developing machine learning models capable of effectively predicting stroke occurrences. Among the models examined, the Random Forest model demonstrated the most promising performance, achieving precision, recall, and F1-score metrics of 90%. These findings hold potential utility for healthcare professionals involved in stroke diagnosis and treatment.

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