This study has modeled numerous confounding parameters of seminal quality for the purpose of digging out the hidden relationship between these seminal parameters using Bayesian Belief Network (BBN). The data source for this study was retrieved from UCI machine learning repository. Etiological patterns were derived out of complex relationship of nine related attributes. We have shown that as compared to conventional statistical measures, BBN is quite useful in analysis of seminal quality as well as classifying an unknown instance. The outcome is composed of a predictive probabilistic model which can classify any new instance whether the seminal quality is altered or not. The observed accuracy of the model is highest (91%) whereas the previous highest accuracy was reported to be 86% only.
목차
Abstract 1. Introduction 2. Materials and Methods 3. Seminal Quality 4. Result 5. Discussion 5.1 Age 5.2 Smoking 5.3 Hours Spent Sitting 5.4 Disease 5.5 Alcoholism 6. Conclusion References
보안공학연구지원센터(IJBSBT) [Science & Engineering Research Support Center, Republic of Korea(IJBSBT)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Bio-Science and Bio-Technology
간기
격월간
pISSN
2233-7849
수록기간
2009~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Bio-Science and Bio-Technology Vol.6 No.6