DEVELOPING BLOOD PRESSURE MODEL USING MACHINE LEARNING ALGORITHM

DEVELOPING BLOOD PRESSURE MODEL USING MACHINE LEARNING ALGORITHM

Abstract:
High blood pressure, or hypertension, is a significant global health concern that affects millions of individuals and is a leading risk factor for cardiovascular diseases. Accurate monitoring and prediction of blood pressure levels are crucial for effective management and timely intervention. This research aims to develop a blood pressure model using machine learning algorithms to enhance prediction accuracy and facilitate personalized healthcare.

The proposed model utilises a diverse set of features, including demographic information, medical history, lifestyle factors, and physiological indicators, to capture the complex relationships influencing blood pressure. A comprehensive dataset comprising longitudinal blood pressure measurements and associated patient information is used for training and evaluation.

Various machine learning algorithms, such as support vector machines, random forests, and deep neural networks, are explored to develop the blood pressure model. The algorithms are trained on a subset of the dataset using supervised learning techniques, optimising for precision, recall, and accuracy metrics. Cross-validation techniques are employed to assess the model’s generalisation performance and mitigate overfitting.

To address the challenge of handling missing data, imputation techniques are employed to estimate the values of incomplete features. Feature selection methods, such as correlation analysis and recursive feature elimination, are employed to identify the most informative predictors for blood pressure prediction.

The developed blood pressure models is evaluated using a separate test dataset, comparing its predictions against actual blood pressure measurements. Performance metrics, including mean absolute error, root mean square error, and R-squared, are calculated to assess the model’s accuracy and predictive power. The model’s robustness and generalizability are further examined through external validation using an independent dataset.

The results demonstrate that the developed blood pressure model using machine learning algorithms achieves superior prediction accuracy compared to traditional methods. The model shows promise in capturing the complex dynamics of blood pressure regulation and provides valuable insights to healthcare professionals for early detection, risk stratification, and personalised treatment recommendations.

In conclusion, this research contributes to the advancement of blood pressure prediction and management by developing a robust and accurate model using machine learning algorithms. The proposed model has the potential to enhance clinical decision-making, improve patient outcomes, and guide the development of innovative interventions for hypertension. Further research is warranted to refine and validate the model in diverse populations and healthcare settings.

DEVELOPING BLOOD PRESSURE MODELS USING MACHINE LEARNING ALGORITHMS, GETTING MORE MASTER COMPUTER SCIENCE

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