A hybrid machine learning model combining neural networks and genetic algorithms achieved 95% accuracy in predicting cardiac arrest based on clinical risk factors.
Does a hybrid genetic machine learning algorithm improve the prediction accuracy of cardiac arrest based on clinical risk factors compared to current techniques?
A hybrid machine learning model combining genetic algorithms and neural networks achieved 95% accuracy in predicting cardiac arrest based on clinical risk factors.
Cardiac arrest is the world's mainreason for fatality. A boring job is the diagnosis of heart failure. For disease prediction, an intelligent system for disease detection is prime necessity. Todecide whether a sufferer is healthy or has illness, data mining methods are also used. In intelligent medical system for estimation and detection of different illness with good correctness, data mining methods have been commonly used. These methods were particularly useful in developing structures of health support because they were able to uncover occulted patterns and associations in patient records. One of the most necessaryuse of likewisemachines is the detection of cardiacillness since it is one of the world's mainreason of death. Almost all programmed that forecast cardiac disorders use clinical data with parameters and inputs from complex laboratory experiments. Neither system forecasts cardiovascular disorders that rely on concernedissues like age, diabetes, high-cholesterol, family background, consuming cigarettes, alcohol, physical inactivity,obesity, etc. Patients of cardiac arrest have various visible risk characteristics in common and can very easily be used for detection. Method focused on risk factors of this type will not only support medical practitioners, but also alert patients of the possible occurrence of heart disease when they go to the hospital or receive expensive medical exams. This paper also supplies a predictive strategy for cardiac arrest using important health symptoms. The methodhasmultiple most useful methods for data processing, neural networks and naïve bays. Our proposed model claims that hybrid systems should be used for classification of heart disease (prediction). The hybrid method used for the initialization of neural network weights uses the whole optimization advantage of the genetic algorithm. Learning is faster, more reliable and right than context spread. Our experimental findings in the data collection for coronary diseases prove that the hybrid method reports 95% accuracy and improves current techniques. This technique can be used by medical practitioners to expect the heart arrest successfully long before it does occur and to save many patient lives,
Sinha et al. (Fri,) conducted a other in Cardiac arrest / Heart disease. Hybrid machine learning model (neural network and genetic algorithm) vs. Current techniques was evaluated on Prediction accuracy. A hybrid machine learning model combining neural networks and genetic algorithms achieved 95% accuracy in predicting cardiac arrest based on clinical risk factors.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: