Algorithmic Insights into Predicting Hypertension Using Health Data in Cloud-Based Environments
Keywords:
Hypertension Prediction, Cloud-Based Healthcare, Advanced Algorithms, Neural Network, Predictive AnalyticsAbstract
This exploration examines the use of cutting-edge calculations for anticipating hypertension inside cloud-based health conditions. Utilizing assorted health information sources, including electronic health records and wearables, we investigated the prescient abilities of four key calculations: Strategic Relapse, Random Forest, Backing Vector Machine (SVM), and Neural Network (Multi-facet Perceptron). Our exploratory arrangement included thorough information preprocessing, highlight extraction, and model preparation on an extensive dataset. The Neural Network arose as the best calculation, accomplishing an exactness of 90%, accuracy of 92%, review of 88%, F1 score of 90%, and an AUC-ROC of 0.94. Random Forest and SVM likewise exhibited hearty execution with a precision of 88% and 87%, individually. Calculated Relapse, however less difficult, displayed cutthroat dependability with a precision of 85%. Correlations with related work highlighted the adaptability of the calculations, reaching out past unambiguous medical services spaces. This exploration adds to the more extensive talk on prescient medical services examination, stressing the reconciliation of cutting-edge calculations in cloud-based conditions. Our findings set the stage for subsequent research, which may include the continuous observation of IoT devices and the improvement of profound learning designs, all while recognizing specific constraints like the representativeness of the dataset and the model's interpretability.
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