scholarly journals Correction to: Machine Learning on Medical Dataset

2019 ◽  
pp. C1-C1
Author(s):  
M. P. Gopinath ◽  
S. L. Aarthy ◽  
Aditya Manchanda ◽  
Rishabh
Keyword(s):  
Machine Learning ◽  
Medical Dataset

scholarly journals Advanced Data Imputation Techniques for Predicting Type 2 Diabetes using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4142-4149
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Missing Data ◽  
Early Stage ◽  
Research Work ◽  
Model Complexity ◽  
Bias Model ◽  
Proposed Model ◽  
Medical Dataset

Type 2 Diabetes mellitus is a serious metabolic disorder that is prevailing worldwide at an alarming rate. Medical dataset often suffers from the problem of missing data and outliers. However, handling of missing data with traditional mean based imputing may lead towards a bias model and return unpredictable outcome. Making complex models by combining multiple classifiers as well as some other methods could increase the accuracy which again is a time-consuming approach and requires heavy computation capability which significantly increases the deployment cost. The proposed research is to design a model to classify the data using class wise imputation technique and outlier handling. Performance of the proposed model is evaluated on nine machine learning classifiers and compared with traditional approaches like simple mean, median, and linear regression. Experimental results show the superiority of the proposed model in terms of classification accuracy and model complexity. The accuracy achieved by the proposed approach is 88.01%, which is highest as compared to the previous studies. The proposed research work is presented to improve accuracy, scalability and overall performance of the classification in the medical dataset, which ultimately proves to be a lifesaver if the diagnosis is achieved efficiently at an early stage.

scholarly journals Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
C. V. Subbulakshmi ◽  
S. N. Deepa
Keyword(s):  
Machine Learning ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Learning Paradigm ◽  
Swarm Optimization ◽  
Generalization Performance ◽  
Learning Machine ◽  
Hidden Layer ◽  
Medical Dataset

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

scholarly journals An Efficient Technique for Disease Prediction by Using Enhanced Machine Learning Algorithms for Categorical Medical Dataset

2021 ◽  
Vol 50 (1) ◽  
pp. 102-122
Author(s):  
Veera Anusuya ◽  
V Gomathi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Large Datasets ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Disease Prediction ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Prediction Mechanism ◽  
Medical Dataset

In the 20th century, it is evident that there is a massive evolution of chronic diseases. The data mining approaches beneficial in making some medicinal decisions for curing diseases. But medical data may consist of a large number of data, which makes the prediction process a very difficult one. Also, in the medical field, the dataset may involve both the small database and extensive database. This creates the study of a complex one for disease prediction mechanism. Hence, in this paper, we intend to use a practical machine learning approach for disease prediction of both large and small datasets. Among the various machine learning procedures, classification, and clusters method play a significant role. Therefore, we introduced the enhanced classification and clusters approach in this work for obtaining better accuracy results for disease prediction. In this proposed method, a process of preprocessing is involved, followed by Eigen vector extraction, feature selection, and classification Further, the most suitable features are selected with the use of Multi-Objective based Ant Colony Optimization (MO-ACO) from the extracted features for increasing the classification and clusters. Here we have shown the novelty in every stage of the implementation, such as feature selection, feature extraction, and the final prediction stage. The proposed method will be compared with the existing technique on the measure of precision, NMI, execution time, recall, and Accuracy. Here we conclude with the solution having more accuracy for both small and large datasets.

Machine Learning on Medical Dataset

2018 ◽  
pp. 133-143
Author(s):  
M. P. Gopinath ◽  
S. L. Aarthy ◽  
Aditya Manchanda ◽  
Rishabh
Keyword(s):  
Machine Learning ◽  
Medical Dataset

scholarly journals Data Balancing Method for Training Segmentation Neural Networks

2020 ◽  
pp. short19-1-short19-9
Author(s):  
Alexey Kochkarev ◽  
Alexander Khvostikov ◽  
Dmitry Korshunov ◽  
Andrey Krylov ◽  
Mikhail Boguslavskiy
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Training Data ◽  
Learning Ability ◽  
Unbalanced Data ◽  
Distance Transform ◽  
Machine Learning Model ◽  
Overall Performance ◽  
Medical Dataset

Data imbalance is a common problem in machine learning and image processing. The lack of training data for the rarest classes can lead to worse learning ability and negatively affect the quality of segmentation. In this paper, we focus on the problem of data balancing for the task of image segmentation. We review major trends in handling unbalanced data and propose a new method for data balancing, based on Distance Transform. This method is designed for using in segmentation convolutional neural networks (CNNs), but it is universal and can be used with any patch-based segmentation machine learning model. The evaluation of the proposed data balancing method is performed on two datasets. The first is medical dataset LiTS, containing CT images of liver with tumor abnormalities. The second one is a geological dataset, containing of photographs of polished sections of different ores. The proposed algorithm enhances the data balance between classes and improves the overall performance of CNN model.

scholarly journals A Survey: Effective Machine Learning Based Classification Algorithm for Medical Dataset

2021 ◽  
Vol 3 (Special Issue 9S) ◽  
pp. 28-33
Author(s):  
Mahalakshmi G. ◽  
Shimaali Riyasudeen ◽  
Sairam R ◽  
Hari Sanjeevi R ◽  
Raghupathy B.
Keyword(s):  
Machine Learning ◽  
Classification Algorithm ◽  
Medical Dataset

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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