Machine Learning based Approaches for Accurately Diagnosis and Detection of Hypertension Disease

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.