Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{178972,
        author = {Dr D P Singh},
        title = {A Comparative Analysis of Machine Learning Techniques for Hypertension Risk Prediction and Diagnostic Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7183-7195},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178972},
        abstract = {Hypertension is a significant contributor to cardiovascular diseases, necessitating early prediction and precise diagnostic classification for effective clinical intervention and prevention strategies. This research conducts an in-depth comparative analysis of multiple machine learning (ML) models for predicting hypertension risk and classifying diagnoses. The performance of various supervised learning algorithms—such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, and Multilayer Perceptron (MLP) is assessed using a clinically validated hypertension dataset.
The evaluation of the models is conducted using essential performance indicators, including Accuracy, Precision, Recall, F1-score, and the Area Under the Curve (AUC), to ensure a comprehensive assessment. Prior to modeling, the dataset undergoes pre-processing steps such as feature selection, normalization, and imputation of missing values to improve model performance. The findings indicate that ensemble techniques like CatBoost, Gradient Boosting, Random Forest, and AdaBoost surpass conventional classifiers in terms of both predictive accuracy and diagnostic consistency.
The research emphasizes the capability of advanced machine learning models to assist healthcare professionals in making timely, data-informed decisions for managing hypertension. It also highlights the critical role of model interpretability and clinical relevance in effectively implementing ML-based diagnostic tools in practical medical environments.},
        keywords = {Classification Models, Comparative Analysis, Diagnostic Classification, Health Informatics, Hypertension, Machine Learning, Medical Diagnosis, Predictive Analytics, Risk Prediction.},
        month = {May},
        }