PMID- 33322123
OWN - NLM
STAT- MEDLINE
DCOM- 20210208
LR  - 20210208
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 17
IP  - 24
DP  - 2020 Dec 13
TI  - Classification of Biodegradable Substances Using Balanced Random Trees and 
      Boosted C5.0 Decision Trees.
LID - 10.3390/ijerph17249322 [doi]
LID - 9322
AB  - Substances that do not degrade over time have proven to be harmful to the 
      environment and are dangerous to living organisms. Being able to predict the 
      biodegradability of substances without costly experiments is useful. Recently, 
      the quantitative structure-activity relationship (QSAR) models have proposed 
      effective solutions to this problem. However, the molecular descriptor datasets 
      usually suffer from the problems of unbalanced class distribution, which 
      adversely affects the efficiency and generalization of the derived models. 
      Accordingly, this study aims at validating the performances of balanced random 
      trees (RTs) and boosted C5.0 decision trees (DTs) to construct QSAR models to 
      classify the ready biodegradation of substances and their abilities to deal with 
      unbalanced data. The balanced RTs model algorithm builds individual trees using 
      balanced bootstrap samples, while the boosted C5.0 DT is modeled using 
      cost-sensitive learning. We employed the two-dimensional molecular descriptor 
      dataset, which is publicly available through the University of California, Irvine 
      (UCI) machine learning repository. The molecular descriptors were ranked 
      according to their contributions to the balanced RTs classification process. The 
      performance of the proposed models was compared with previously reported results. 
      Based on the statistical measures, the experimental results showed that the 
      proposed models outperform the classification results of the support vector 
      machine (SVM), K-nearest neighbors (KNN), and discrimination analysis (DA). 
      Classification measures were analyzed in terms of accuracy, sensitivity, 
      specificity, precision, false positive rate, false negative rate, F1 score, 
      receiver operating characteristic (ROC) curve, and area under the ROC curve 
      (AUROC).
FAU - Elsayad, Alaa M
AU  - Elsayad AM
AUID- ORCID: 0000-0001-8053-9759
AD  - Department of Electrical Engineering, College of Engineering, Prince Sattam Bin 
      Abdulaziz University, P.O. Box 54, Wadi Aldawaser 11991, Saudi Arabia.
AD  - Computers and Systems Department, Electronics Research Institute, Giza 12622, 
      Egypt.
FAU - Nassef, Ahmed M
AU  - Nassef AM
AUID- ORCID: 0000-0001-9604-5737
AD  - Department of Electrical Engineering, College of Engineering, Prince Sattam Bin 
      Abdulaziz University, P.O. Box 54, Wadi Aldawaser 11991, Saudi Arabia.
AD  - Department of Computers and Automatic Control Engineering, Faculty of 
      Engineering, Tanta University, Tanta 31733, Egypt.
FAU - Al-Dhaifallah, Mujahed
AU  - Al-Dhaifallah M
AUID- ORCID: 0000-0002-8441-2146
AD  - Systems Engineering Department, King Fahd University of Petroleum & Minerals, 
      Dhahran 31261, Saudi Arabia.
FAU - Elsayad, Khaled A
AU  - Elsayad KA
AD  - Pharmacy Department, Cairo University Hospitals, Cairo University, Cairo 11662, 
      Egypt.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201213
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Algorithms
MH  - *Decision Trees
MH  - Discriminant Analysis
MH  - Humans
MH  - *Machine Learning
MH  - ROC Curve
MH  - *Support Vector Machine
PMC - PMC7763457
OTO - NOTNLM
OT  - C5.0 decision tree
OT  - K-nearest neighbors
OT  - QSAR
OT  - biodegradable substances
OT  - discrimination analysis
OT  - machine learning
OT  - random trees
OT  - support vector machine
COIS- The authors declare no conflict of interest.
EDAT- 2020/12/17 06:00
MHDA- 2021/02/09 06:00
PMCR- 2020/12/01
CRDT- 2020/12/16 01:02
PHST- 2020/10/18 00:00 [received]
PHST- 2020/11/28 00:00 [revised]
PHST- 2020/12/11 00:00 [accepted]
PHST- 2020/12/16 01:02 [entrez]
PHST- 2020/12/17 06:00 [pubmed]
PHST- 2021/02/09 06:00 [medline]
PHST- 2020/12/01 00:00 [pmc-release]
AID - ijerph17249322 [pii]
AID - ijerph-17-09322 [pii]
AID - 10.3390/ijerph17249322 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2020 Dec 13;17(24):9322. doi: 
      10.3390/ijerph17249322.