Investigation On Heart Disease Using Machine Learning Algorithms
| Autor: | M. Pavithraa, D. Selvakarthi, L. Rahunathan, S.Nandhini Eswari, M. Sridhar, D. Sivabalaselvamani |
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| Rok vydání: | 2021 |
| Předmět: |
Heart disease
Computer science business.industry CAD 02 engineering and technology Solid modeling Coronary disease 021001 nanoscience & nanotechnology Machine learning computer.software_genre medicine.disease Perceptron Coronary artery disease Prediction algorithms Path (graph theory) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | 2021 International Conference on Computer Communication and Informatics (ICCCI). |
| DOI: | 10.1109/iccci50826.2021.9402390 |
| Popis: | Coronary delivery path coronary disease is caused in coronary corridors by atherosclerosis which results in heart failure and respiratory failure. Angiography, an exorbitantly tedious and deeply specialized intrusive technique, is used to conclude CAD (Coronary Artery Disease). Thus, experts are triggered by elective approaches, for example, AI calculations that will use non-intrusive scientific knowledge to assess and measure the severity of the cardiovascular condition. In this report, we present another way to deal with CAD examination combination, including recognizable peril factor realities utilizing the atom, swam upgrade mission strategy, and K-Means bundling computation decision of the relationship-based segment subset. The WEKA instrument is an open-source apparatus utilized for information pre-handling, grouping, and arranging. Overseen learning recreations, for example, multi-layer perceptron, multinomial strategic relapse, fluffy unordered principle acceptance calculation, and C4.5, are then used to exhibit CAD situations. |
| Databáze: | OpenAIRE |
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