Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset
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Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset Thippa Reddy G1 · Sweta Bhattacharya1 · Praveen Kumar Reddy Maddikunta1 · Saqib Hakak2 · Wazir Zada Khan3 · Ali Kashif Bashir4 · Alireza Jolfaei5 · Usman Tariq6 Received: 21 March 2020 / Revised: 20 July 2020 / Accepted: 24 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Stroke is enlisted as one of the leading causes of death and serious disability affecting millions of human lives across the world with high possibilities of becoming an epidemic in the next few decades. Timely detection and prompt decision making pertinent to this disease, plays a major role which can reduce chances of brain death, paralysis and other resultant outcomes. Machine learning algorithms have been a popular choice for the diagnosis, analysis and predication of this disease but there exists issues related to data quality as they are collected cross-institutional resources. The present study focuses on improving the quality of stroke data implementing a rigorous pre-processing technique. The present study uses a multimodal stroke dataset available in the publicly available Kaggle repository. The missing values in this dataset are replaced with attribute means and LabelEncoder technique is applied to achieve homogeneity. However the dataset considered was observed to be imbalanced which reflect that the results may not represent the actual accuracy and would be biased. In order to overcome this imbalance, resampling technique was used. In case of oversampling, some data points in the minority class are replicated to increase the cardinality value and rebalance the dataset. transformed and oversampled data is further normalized using Standardscalar technique. Antlion optimization (ALO) algorithm is implemented on the deep neural network (DNN) model to select optimal hyperparameters in minimal time consumption. The proposed model consumed only 38.13% of the training time which was also a positive aspect. The experimental results proved the superiority of proposed model. Keywords Deep neural networks · Antlion optimization · Stroke prediction · Re-sampling · Imbalanced dataset
Wazir Zada Khan
[email protected]
Extended author information available on the last page of the article.
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1 Introduction The statistical report of WHO (World Health Organization) has identified stroke to be one of the predominant causes of disability in the world wherein an estimated 17 million people succumb to death, being a victim of heart disease and strokes. The primary reasons for individuals getting affected by heart diseases, almost giving it the status of an epidemic are physical inactivity, unhealthy and irregular lifestyle and tobacco smoking. In United States it is the enlisted within the top five reasons of death across advanced aged male and females every year. Naturally, there is a proportional increase in the medical expenses of an estimated 23 bil
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