Robust Detection of Cardiac Disease Using Machine Learning Algorithms

Domyati, Anas; Memon, Qurban A.

doi:10.1145/3561613.3561622

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…A possible architecture of the embedded system is discussed in section five, and conclusions are discussed in section six. It should be noted here that the preliminary results of this research are reported in [21].…”

Section: Literature Reviewmentioning

confidence: 80%

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: 80%

Machine Learning Based Improved Heart Disease Detection with Confidence

Domyati¹,

Memon²

2023

Int. J. Onl. Eng.

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“…This method includes modeling single trip patterns in terms of time-window, traveling-distance, and speed; and developing vehicle mobility profiles of trip combinations. For further readings on clustering, the readers are referred to references 6,7 .…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering vehicle movement in restricted area for security: an experimental analysis

Memon

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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Vehicle access in restricted or protected areas of a country involves phase wise collection of vehicle data in areas like diplomatic enclave. Thus, data gathering at each street junction produces an output that forms the input to the next junction. The analysis system thus developed is intended to be used by government agencies and other special task force to provide sound reports for legal proceeding against violations. In this research, two approaches namely SOM and K-means clustering are used to generate a low-dimensional discretized representation of the vehicle data in the original feature space. The objective is to generate access control for security purposes by providing an automated vehicle analysis based upon clustered data for quicker information on vehicle movement in a particular neighborhood. Comprehensive experimental results are presented to draw conclusions.

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