HEART DISEASE PREDICTION USING MULTILAYER PERCEPTRON IN A DYNAMIC ENVIRONMENT
Le Thi My Nhu , Ngo Ho Anh Khoi , Duong Duy Khanh
Abstract: In recent years, the incidence and mortality rates due to cardiovascular diseases have been on the rise globally. This is the primary reason why the main objective of this topic is to investigate techniques aimed at solving the problem of heart disease diagnosis. The research methodology for this topic involves the use of the scientific experimental approach, conducted on the Multilayer Perceptron (MLP) algorithm using the Heart Failure Prediction Dataset as the foundational dataset. This research addresses a highly significant societal issue. If further studied and developed, it has the potential to empower individuals to proactively and effectively prevent heart diseases. The prediction of heart disease has become a crucial field of study, aiding in early detection, risk assessment, and the implementation of preventive measures. This article summarizes several important aspects related to heart disease prediction based on scientific machine learning methods.
Keywords: Heart Failure Prediction Dataset, HEART DISEASE PREDICTION, Multilayer Perceptron, MLP
Attribute Data
| Attribute | Converted Value |
|---|---|
| Sex | Male: 1, Female: 0 |
| ExerciseAngina | Y: 1, N: 0 |
| ChestPainType | ASY: 1, TA: 2, ATA: 3, NAP: 4 |
| FastingBS | Greater than 120: 1, Otherwise: 0 |
| RestingECG | Normal: 1, ST: 2, LVH: 3 |
| ST_Slope | Up: 1, Flat: 2, Down: 3 |
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