Predicting Divorce Prospect Using Ensemble Learning: Support Vector Machine, Linear Model, and Neural Network.
Comput Intell Neurosci
; 2022: 3687598, 2022.
Article
Dans Anglais
| 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.
Texte intégral:
Disponible
Collection:
Bases de données internationales
Base de données:
MEDLINE
Sujet Principal:
Divorce
/
Machine à vecteur de support
Type d'étude:
Études expérimentales
/
Étude pronostique
/
Essai contrôlé randomisé
Limites du sujet:
Femelle
/
Humains
Pays comme sujet:
Amérique du Nord
langue:
Anglais
Revue:
Comput Intell Neurosci
Thème du journal:
Informatique médicale
/
Neurologie
Année:
2022
Type de document:
Article
Pays d'affiliation:
2022
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