Classification of Infarction using Random Forest

Stroke is a condition caused by disruption in the blood supply to the brain. When the flow of blood is decreasing and resulting dead brain tissue that is called an infarction. If this condition not treated immediately and don’t get the right treatment will cause the death of the brain. Therefore, the classification of infarction is important to help increase the life expectancy of the patients. In this study, we are using infarction data from the Department of Radiology at Dr. Cipto Mangunkusumo Hospital and propose a random forest method to help the health sector for detecting infarction quickly and accurately. The best result by using the random forest method is 94.44 percent with 65 percent as training data.

[1]  Zuherman Rustam,et al.  Hybrid Preprocessing Method for Support Vector Machine for Classification of Imbalanced Cerebral Infarction Datasets , 2019 .

[2]  Zuherman Rustam,et al.  Feature Selection using Random Forest Classifier for Predicting Prostate Cancer , 2019, IOP Conference Series: Materials Science and Engineering.

[3]  Devvi Sarwinda,et al.  Random-Forest (RF) and Support Vector Machine (SVM) Implementation for Analysis of Gene Expression Data in Chronic Kidney Disease (CKD) , 2019, IOP Conference Series: Materials Science and Engineering.

[4]  Zuherman Rustam,et al.  Soft Tissue Tumor Classification using Stochastic Support Vector Machine , 2019, IOP Conference Series: Materials Science and Engineering.

[5]  Zuherman Rustam,et al.  Classification of cancer data using support vector machines with features selection method based on global artificial bee colony , 2018 .

[6]  Zuherman Rustam,et al.  Comparison of Cubic SVM with Gaussian SVM: Classification of Infarction for detecting Ischemic Stroke , 2019, IOP Conference Series: Materials Science and Engineering.

[7]  V. Feigin,et al.  Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010 , 2014, The Lancet.

[8]  Xinyi Hu,et al.  Block Ciphers Classification Based on Random Forest , 2019 .

[9]  Cuong Nguyen,et al.  Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic , 2013 .

[10]  Zuherman Rustam,et al.  Classification of Breast Cancer using Fast Fuzzy Clustering based on Kernel , 2019, IOP Conference Series: Materials Science and Engineering.

[11]  Zuherman Rustam,et al.  Osteoarthritis Disease Prediction Based on Random Forest , 2018, 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[12]  Zuherman Rustam,et al.  Application of machine learning on brain cancer multiclass classification , 2017 .

[13]  M. Elkind,et al.  Stroke Risk Factors, Genetics, and Prevention , 2017, Circulation research.