Carrying out efficient classification of ovarian cancer involves recognizing the key features or patterns from the collected dataset, and selecting attributes that is more relevant in relation to ovarian cancer disease, and comparing Support Vector Machine, random forest, and decision tree
	GOKULAKRISHNAN D; REDDY K C V; VIJAYARAGAVAN S P; KARTHIK B; HEMALATHA B
	2023-10-20
专利权人	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
申请日期	2023-10-20
专利号	IN202341071653-A
成果简介	NOVELTY - Carrying out efficient classification of ovarian cancer involves: (a) recognizing the key features or patterns from the collected dataset, and selecting attributes that is more relevant in relation to Ovarian Cancer disease; (b) comparing Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT) and Convolutional Neural Network (CNN) in predicting Ovarian Cancer disease. USE - Method for carrying out efficient classification of ovarian cancer. ADVANTAGE - The method allows to understand and evaluate the outcomes of the selected model with the help of field expert, reduce the risk of inaccuracy and to obtain the correct diagnosis, and the study implement computer technology in pathologic prognosis.
IPC 分类号	C12Q-001/6886 ; G01N-033/574 ; G06K-009/62 ; G06N-020/10 ; G06N-005/00
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/19479
专题	中国科学院新疆生态与地理研究所
作者单位	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
推荐引用方式 GB/T 7714	GOKULAKRISHNAN D,REDDY K C V,VIJAYARAGAVAN S P,et al. Carrying out efficient classification of ovarian cancer involves recognizing the key features or patterns from the collected dataset, and selecting attributes that is more relevant in relation to ovarian cancer disease, and comparing Support Vector Machine, random forest, and decision tree. IN202341071653-A[P]. 2023.

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