Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network.
Comput Intell Neurosci
; 2022: 3687598, 2022.
Article
in English
| MEDLINE | ID: covidwho-1962471
ABSTRACT
A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Divorce
/
Support Vector Machine
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Female
/
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Comput Intell Neurosci
Journal subject:
Medical Informatics
/
Neurology
Year:
2022
Document Type:
Article
Affiliation country:
2022
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