Information Cardiac Platform to Support Healthcare

Georgieva-Tsaneva, Galya; Gospodinova, Еvgeniya; Bogdanova, Galina; Dimitrova, Diana

doi:10.3991/ijoe.v19i03.35217

Cited by 1 publication

(1 citation statement)

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“…In a different study, the authors Ali et al [18] pay close attention to every facet of performance, where the removal of features that do not contribute sufficiently and the use of layered genetic algorithms are recommended with an accuracy of over 90%. For recent study on this subject, reader is referred to works in references [19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Based Improved Heart Disease Detection with Confidence

Domyati¹,

Memon²

2023

Int. J. Onl. Eng.

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One of the hardest jobs in medicine is to predict when someone will have a heart attack. Given how challenging it is to anticipate heart attack, there is an urgent need to automate the prediction process using diagnostic data, and at the very least generate an early warning. This research makes a contribution by making it easier to diagnose cardiac problems using machine learning methods applied on the well-known Cleveland heart disease dataset. Several performance indicators are utilized to evaluate each model's strength. It turns out that support vector machine and random forest produced some incredibly promising outcomes. An improved prediction of heart disease for an embedded platform is, thus, proposed, based on the computational complexity of each model and experimental results, where the advantages of several classifiers are accumulated. The approach suggests that, and only if, more than one of these classifiers detect heart disease, the detection of heart illness is possible with increased confidence. In the end, experimental findings are drawn to a conclusion, with potential future options for advancing this effort.

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Section: Literature Reviewmentioning

confidence: 99%