Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm

Background: Multiple sclerosis (MS) is a degenerative inflammatory disease which is most commonly diagnosed by magnetic resonance imaging (MRI). But, since the MRI device uses of a magnetic field, if there are metal objects in the patient's body, it can disrupt the health of the patient, the functioning of the MRI, and distortion in the images. Due to limitations of using MRI device, screening ...

Predicting the cause of kidney stones in patients using random forest, support vector machine and neural network

Background: Today, with the advancement of technology in various fields, the importance of recording data in the field of health is increasing so much that for many diseases around the world, including kidney disease, registration systems have been set up. This is happening in our country and in the future, the number of these systems will increase. The medical data set contains valuable inform...

Optimal Decision Tree Based Multi-class Support Vector Machine

Predicting Disease Risks Using Feature Selection Based on Random Forest and Support Vector Machine

Disease risk prediction is an important task in biomedicine and bioinformatics. To resolve the problem of high-dimensional features space and highly feature redundancy and to improve the intelligibility of data mining results, a new wrapper method of feature selection based on random forest variables importance measures and support vector machine was proposed. The proposed method combined seque...

Comparative Prediction Performance with Support Vector Machine and Random Forest Classification TechniquesComparative Prediction Performance with Support Vector Machine and Random Forest Classification Techniques

Machine learning with classification can effectively be applied for many applications, especially those with complex measurements. Therefore classification technique can be used for prediction of diseases like cancer, liver disorders and heart disease etc which involve complex measurements. This is part of growing demand and much interesting towards predictive diagnosis. It has also been establ...