Development and Validation of a Robust Predictive Model for Early Detection  of Heart Failure in Anfoega-Ghanaian Patients.

Author(s)	Mr. Frederick Quarshie, Prof. Christina Cynthia Nyarko, Dr. Benjamin Odoi
Country	Ghana
Abstract	This research highlights critical risk factors linked to heart failure employing an advanced statistical model for proactive identification. The analysis shows that time, serum creatinine, ejection fraction, age, distance to the nearest health center, cost of food, angina, disability, and maximum heart rate contribute significantly to heart failure. Out of all these, the three most salient predictors are time, ejection fraction, and serum creatinine. Findings from the research indicate heart failure can occur at any age, but the disease is more common in older individuals. Thus, constant evaluation of these vital signs is an important requirement. Patients are advised to schedule routine check-ups and health care practitioners need to provide optimal advice and care relative to the degree of risk exposure.
Keywords	Heart failure, Prediction, Detection, Biomarkers, Cardiovascular, Machine Learning, turn-around cost, Distance, Non-communicable, Oxygenation
Field	Mathematics > Statistics
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.54921

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