Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Veena Kumari H M
Dengue infection is caused by the mosquito Aedes aegypti. According to WHO, 50 to 100 million dengue infections will occur every year. Data-miming techniques will extract information from the raw data. Dengue symptoms are fever, severe headache, body pain, vomiting, diarrhoea, cough, pain in the abdomen, etc. The research work is carried out on real data and the patient data is collected from the Department of General Medicine, PESIMSR, Kuppam, Andrapradesh. Dataset consists of 18 attributes and one target value. Research work has been done on a binary classification to classify dengue positive (DF) and dengue negative (NDF) cases using different ML techniques. The proposed work demonstrates that ensemble techniques of bagging, boosting, and stacking give better results than other models. The Extreme Gradient Boost (XGB), Random Forest by majority voting, and stacking with different meta-classifiers are the ensemble techniques used for binary classification. The dataset is divided into 80% training and 20 % testing dataset. Performance parameters used for the analysis are accuracy, precision, recall, and f1 score, and compared the proposed model with other ML models.
Veena Kumari H M. 2026. \u201cClinical Dengue Data Analysis and Prediction using Multiple Classifiers: An Ensemble Techniques\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 22 (GJCST Volume 22 Issue D2): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Dengue infection is caused by the mosquito Aedes aegypti. According to WHO, 50 to 100 million dengue infections will occur every year. Data-miming techniques will extract information from the raw data. Dengue symptoms are fever, severe headache, body pain, vomiting, diarrhoea, cough, pain in the abdomen, etc. The research work is carried out on real data and the patient data is collected from the Department of General Medicine, PESIMSR, Kuppam, Andrapradesh. Dataset consists of 18 attributes and one target value. Research work has been done on a binary classification to classify dengue positive (DF) and dengue negative (NDF) cases using different ML techniques. The proposed work demonstrates that ensemble techniques of bagging, boosting, and stacking give better results than other models. The Extreme Gradient Boost (XGB), Random Forest by majority voting, and stacking with different meta-classifiers are the ensemble techniques used for binary classification. The dataset is divided into 80% training and 20 % testing dataset. Performance parameters used for the analysis are accuracy, precision, recall, and f1 score, and compared the proposed model with other ML models.
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