Automated Cardiovascular Disease Diagnosis using Honey Badger Optimization with Modified Deep Learning Model

The leading cause of death among people around the world is cardiovascular disease (CVD). In order to prevent patients from other damages, precise diagnostics CVD on time a crucial factor. Researcher workers are inspired apply machine learning (ML) for accurate and quick diagnosis CVD. ML algorithm extracts patterns hidden relationships in medical dataset detecting or predicting development. But prediction challenging task. increasing size healthcare has made it complex task practitioners make predictions understand feature relations. And so, selection features plays key role optimizing performance algorithm. This study develops an Automated Cardiovascular Disease Diagnosis using Honey Badger Optimization with Modified Deep Learning (ACVD-HBOMDL) Model. major aim ACVD-HBOMDL technique lies classification (FS) hyperparameter tuning strategies. Initially, applies min-max scaler preprocess data. To elect optimal subset features, HBO used this work. For classification, deep modified neural network (DLMNN) classifier its hyperparameters can be optimally chosen by Bayesian optimization. experimental results tested benchmark obtained demonstrate significant outcomes over existing techniques.