Key result
Random forest and KNN machine learning models accurately detect hypertension from symptom and investigation features.
Why the study?
Accurate prediction of hypertension is essential for early intervention and disease management, making it important to understand how machine learning models are used, their performance, and associated challenges.
Do machine learning models accurately detect and diagnose hypertension?
Do machine learning models accurately detect and diagnose hypertension?
Machine learning models, particularly random forest and KNN, show promise in accurately detecting and diagnosing hypertension.
ML models may support hypertension detection research; leaves open prospective validation before any clinical adoption.
Predicting hypertension accurately is essential for early intervention and effective disease management. In recent years, machine learning techniques have attracted considerable interest for their potential to predict and diagnose a variety of medical conditions, including hypertension. The purpose of this article is to provide an insight into how machine learning models are used for hypertension, emphasizing the methodologies employed, performance metrics, and difficulties encountered. In the article, to properly analyze the disease, symptoms and investigations are taken into consideration to pre-process the disease features. After pre-processing, feature scaling is applied to optimize the disease prediction results. Further, machine learning-based models are used to classify the disease and determine whether the person has hypertension issues or not. Based on our analysis, we concluded that the random forest and KNN models accurately detect hypertension.
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Kaur et al. (2023) conducted a review in Hypertension. Machine learning models (Random Forest and KNN) was evaluated on Detection of hypertension. Random forest and K-nearest neighbors (KNN) machine learning models accurately detect hypertension based on symptom and investigation features.
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