Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)

https://pure.rug.nl/ws/files/66844988/263.full.pdf

A critical assessment of feature selection methods for biomarker discovery in clinical proteomics

Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.

Feature selection is a preprocessing step of data mining, in which a subset of relevant features is selected for building models. Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient in solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to effectively address feature selection problems. In this paper, we propose an analytical approach by integrating particle swarm optimization (PSO) and the 1-NN method. The data sets collected from UCI machine learning databases were used to evaluate the effectiveness of the proposed approach. Implementation results show that the classification accuracy of the proposed approach is significantly better than those of BPNN, LR, SVM, and C4.5. Furthermore, the proposed approach was applied to an actual case on the diagnosis of obstructive sleep apnea (OSA). After implementation, we conclude that our proposed method can help identify important factors and provide a feasible model for diagnosing medical disease.

Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

http://id.inf.ucv.ro/~cstoean/papers/aiim2011paper.pdf

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C

Although liver biopsy is currently regarded as the gold standard for staging liver fibrosis in chronic hepatitis C, it is a costly invasive procedure and carries a small risk for complication Our aim in this study was to construct a simple model to distinguish between patients with no or mild fibrosis (METAVIR F0---F1) versus those with clinically significant fibrosis (METAVIR F2---F4) We retrospectively studied 204 consecutive CHC patients Thirty-four serum markers with age, gender, duration of infection were assessed to classify fibrosis with a classifier known as the support vector machine (SVM) The method of feature selection known as sequential forward floating selection (SFFS) was introduced before the performance of SVM When four serum markers were extracted with SFFS-SVM, F2---F4 could be predicted accurately in 96% Our study showed that application of this model could identify CHC patients with clinically significant fibrosis with a high degree of accuracy and may decrease the need for liver biopsy

Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C

Today, radio-frequency ablation has been shown to be a safe and effective method to treat paroxysmal tachycardia with Wolff-Parkinson-White syndrome. The many criteria reported for localizing the sites of accessory pathways from a 12-lead electrocardiogram have not proven adequate to differentiate the correct sites of accessory pathways for all situations. This study trained an artificial neural network to differentiate the varied features needed to localize 10 sites of accessory pathways. One hundred fifty patients underwent successful catheter ablation, with manifest single and antegradely conducting accessory pathways. Using the two electrocardiogram features of polarity of delta wave and R wave's share of QRS complex, an artificial neural network learned the characteristics of electrocardiogram waves for each site of the 10 accessory pathways through 90 learning cases, and an applicable network model was developed for testing. In 58 of 60 test cases (96.7%), sites of accessory pathways were localized correctly by the network. Based on the method employed in the present study, it thus becomes possible to predict the sites of accessory pathways with Wolff-Parkinson-White syndrome in more detail by using an artificial neural network with a 12-lead electrocardiogram. In the future, when this method is incorporated into a conventional automatic electrocardiogram system which could analyze delta waves and ORS complex, it will become useful to automatically diagnose the locations of the accessory pathways with Wolff-Parkinson-White syndrome in clinical practice.

Zheng Jiang

Papers

Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C

Use of an artificial neural network to localize accessory pathways of Wolff-Parkinson-White syndrome with 12-lead electrocardiogram.