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A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets
| Content Provider | Semantic Scholar |
|---|---|
| Author | Luukka, Pasi Lampinen, Jouni |
| Copyright Year | 2010 |
| Abstract | In this article we have studied the usage of a classification method based on preprocessing the data first using principal component analysis, and then using the compressed data in actual classification process which is based on differential evolution algorithm, an evolutionary optimization algorithm. This method is applied here for prediction diagnosis from clinical data sets with chief complaint of chest pain using classical Electronic Medical Record (EMR), heart data sets. For experimentation we used a set of five frequently applied benchmark data sets including Cleveland, Hungarian, Long Beach, Switzerland and Statlog data sets. These data sets are containing demographic properties, clinical symptoms, clinical findings, laboratory test results specific electrocardiography (ECG), results pertaining to angina and coronary infarction, etc. In other words, classical EMR data pertaining to the evaluation of a chest pain patient and ruling out angina and/or Coronary Artery Disease, (CAD). The prediction diagnosis results with the proposed classification approach were found promisingly accurate. For example, the Switzerland data set was classified with 94.5 % ±0.4 % accuracy. Combining all these data sets resulted in the classification accuracy of 82.0 % ±0.5 %. We compared the results of the proposed method with the corresponding results of the other methods reported in the literature that have demonstrated relatively high classification performance in solving this problem. Depending on the case, the results of the proposed method were of equal level with the best compared methods, or outperformed their classification accuracy clearly. In general, the results are suggesting that the proposed method has potential in this task. |
| Starting Page | 263 |
| Ending Page | 283 |
| Page Count | 21 |
| File Format | PDF HTM / HTML |
| DOI | 10.1007/978-3-642-12775-5_11 |
| Alternate Webpage(s) | https://page-one.springer.com/pdf/preview/10.1007/978-3-642-12775-5_11 |
| Alternate Webpage(s) | https://doi.org/10.1007/978-3-642-12775-5_11 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
