Classification of diabetes using Naive Bayes and support vector machine as a technique
S Gupta, HK Verma, D Bhardwaj - Operations Management and Systems …, 2021 - Springer
Diabetes is one of the most common disease in today's life. It is affecting people with a high
rate, destroying person's physical, mental, economic and family life. Diabetes is a disease …
rate, destroying person's physical, mental, economic and family life. Diabetes is a disease …
Data mining methods applied to a digital forensics task for supervised machine learning
AJ Tallón-Ballesteros, JC Riquelme - Computational intelligence in digital …, 2014 - Springer
Digital forensics research includes several stages. Once we have collected the data the last
goal is to obtain a model in order to predict the output with unseen data. We focus on …
goal is to obtain a model in order to predict the output with unseen data. We focus on …
[HTML][HTML] An efficient predictive model for myocardial infarction using cost-sensitive j48 model
A Daraei, H Hamidi - Iranian journal of public health, 2017 - ncbi.nlm.nih.gov
Background: Myocardial infarction (MI) occurs due to heart muscle death that costs like
human life, which is higher than the treatment costs. This study aimed to present an MI …
human life, which is higher than the treatment costs. This study aimed to present an MI …
Application of fuzzy logic and genetic algorithm in heart disease risk level prediction
P Sharma, K Saxena - … Journal of System Assurance Engineering and …, 2017 - Springer
As individuals have intrigues in their wellbeing now a days, advancement of therapeutic
area application has been a standout amongst the most dynamic exploration territories. One …
area application has been a standout amongst the most dynamic exploration territories. One …
Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm
The automated identification of brain structure in Magnetic Resonance Imaging is very
important both in neuroscience research and as a possible clinical diagnostic tool. In this …
important both in neuroscience research and as a possible clinical diagnostic tool. In this …
Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems
AJ Tallón-Ballesteros, JC Riquelme, R Ruiz - Neurocomputing, 2019 - Elsevier
This paper explores widely the data preparation stage within the process of knowledge
discovery and data mining via feature subset selection in the context of two very well-known …
discovery and data mining via feature subset selection in the context of two very well-known …
Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks
AJ Tallón-Ballesteros, JC Riquelme, R Ruiz - Connection Science, 2016 - Taylor & Francis
This paper presents a quality enhancement of the selected features by a hybrid filter-based
jointly on feature ranking and feature subset selection (FR-FSS) using a consistency-based …
jointly on feature ranking and feature subset selection (FR-FSS) using a consistency-based …
A bumble bees mating optimization algorithm for the feature selection problem
M Marinaki, Y Marinakis - International Journal of Machine Learning and …, 2016 - Springer
The feature selection problem is an interesting and important topic which is relevant for a
variety of database applications. This paper utilizes a relatively new bees inspired …
variety of database applications. This paper utilizes a relatively new bees inspired …
A feature selection approach based on sensitivity of RBFNNs
X Zeng, Z Zhen, J He, L Han - Neurocomputing, 2018 - Elsevier
Feature selection is an important issue in pattern recognition and machine learning, which
aims at selecting relevant features from a set of candidates. Obviously, the establishment of …
aims at selecting relevant features from a set of candidates. Obviously, the establishment of …
A topological approach for mammographic density classification using a modified synthetic minority over-sampling technique algorithm
I Nedjar, S Mahmoudi… - International Journal of …, 2022 - inderscienceonline.com
Mammographic density is known to be a risk indicator for breast abnormalities development.
Therefore, the breast tissue classification is an important part used in computer aided …
Therefore, the breast tissue classification is an important part used in computer aided …