Medical Data Classification Using Different Optimization Techniques: A Survey

Coronary artery disease (CAD) is the most common cardiovascular disease, causing death all over the world. An invasive method, Angiography is used to diagnose this disease but it is very costly and has some side effects. Hence, non-invasive methods such as machine learning were being used for diagnosing CAD. One of the ways to detect the presence of CAD is to find out the stenotic artery. The proposed study has diagnosed whether the arteries are stenotic or not. This study aims to provide the best accuracy while balancing the dataset using a spreadsubsample filter. Data pre-processing and feature selection has been done on the dataset to improve accuracy. Different supervised classifiers were applied to the selected features. The highest accuracies for left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) obtained by Random Forest are 95.70%, 91.41%, and 94.38% respectively. Among all the arteries, LAD has the highest accuracy indicating that chances of a person having LAD as stenotic are very high.

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Integrating Multiple Techniques to Enhance Medical Data Classification

Tarle¹,

Akkalakshmi

2022

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Improving classification performance is an essential task in medical data classification. In the current medical data classification technique, if data pre-processing is not performed, the approach is more time consuming and has less classification accuracy. Here, the authors proposed two pre-processing techniques for enhancing the classification performance on medical data. The first pre-processing technique is noise filtering to improve the data quality. The second pre-processing bag of words technique is used for better feature selection. Subsequently, the hybrid fuzzy neural network approach is used for classification to handle data imprecision during classification. This arrangement of data pre-processing and the fuzzy neural classifier method improve classification accuracy.

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Medical Data Classification Using Different Optimization Techniques: A Survey

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Cited by 4 publications

References 52 publications

Diagnosis of Disease Using Feature Decimation with Multiple Classifier System

Diagnosis of Disease Using Feature Decimation with Multiple Classifier System

CAD diagnosis by predicting stenosis in arteries using data mining process

Integrating Multiple Techniques to Enhance Medical Data Classification

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