Classification of Schizophrenia Data Using Support Vector Machine (SVM)

Aurelia

et al. 2021

IJ-AI

In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%.

Section: Methodsmentioning

confidence: 99%

Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

Wirasati

Aurelia

et al. 2021

IJ-AI

“…SVM is a computational algorithm that learns to assign labels to object from experience and examples. SVM can be applied to medical diagnosis [17][18][19] weather prediction, finance [20], stock market analysis [21][22] and image processing [23]. SVM has the fundamental feature of separating binary labeled data centered on a line that achieves the labeled data's maximum distance [24].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Linear discriminant analysis and support vector machines for classifying breast cancer

Amalia

Hartini

et al. 2021

IJ-AI

Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.

“…Moreover, with a total of 212 participants which consist of 141 schizophrenia patients and 71 healthy controls, the regularized SVM model gives accuracy of 86.6% in the training set of 127 individuals, meanwhile validation accuracy of 83.5 percent in an independent set of 85 people [9]. Meanwhile, SVM was able to classify the schizophrenia dataset with an accuracy of 90.1 percent and 95.0 percent in at least one simulation using linear and Gaussian kernel [10]. Mean accuracy of more than 90 percent was also obtained using Twin SVM with linear and Gaussian kernel [11].…”

Section: Introductionmentioning

confidence: 98%

The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Hartini

2020

TELKOMNIKA

Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.