Ambient Healthcare Approach with Hybrid Whale Optimization Algorithm and Naïve Bayes Classifier
| Autor: | Kareem Nagy Areed, Majed Alwateer, Mostafa A. Elhosseini, Amira Y. Haikal, Mahmoud Mohammed Badawy, Abdulqader M. Almars |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Big Data
Whale Optimization Computer science Data classification Big data Cloud computing Feature selection TP1-1185 02 engineering and technology computer.software_genre Biochemistry Data type Sonar Article Analytical Chemistry Naive Bayes Naive Bayes classifier feature selection otorhinolaryngologic diseases 0202 electrical engineering electronic engineering information engineering Animals Electrical and Electronic Engineering Instrumentation business.industry Chemical technology Whales Bayes Theorem 020206 networking & telecommunications decision-making Hybrid algorithm Atomic and Molecular Physics and Optics classification 020201 artificial intelligence & image processing Data mining big healthcare data business Delivery of Health Care computer Algorithms |
| Zdroj: | Sensors Volume 21 Issue 13 Sensors, Vol 21, Iss 4579, p 4579 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21134579 |
| Popis: | There is a crucial need to process patient’s data immediately to make a sound decision rapidly this data has a very large size and excessive features. Recently, many cloud-based IoT healthcare systems are proposed in the literature. However, there are still several challenges associated with the processing time and overall system efficiency concerning big healthcare data. This paper introduces a novel approach for processing healthcare data and predicts useful information with the support of the use of minimum computational cost. The main objective is to accept several types of data and improve accuracy and reduce the processing time. The proposed approach uses a hybrid algorithm which will consist of two phases. The first phase aims to minimize the number of features for big data by using the Whale Optimization Algorithm as a feature selection technique. After that, the second phase performs real-time data classification by using Naïve Bayes Classifier. The proposed approach is based on fog Computing for better business agility, better security, deeper insights with privacy, and reduced operation cost. The experimental results demonstrate that the proposed approach can reduce the number of datasets features, improve the accuracy and reduce the processing time. Accuracy enhanced by average rate: 3.6% (3.34 for Diabetes, 2.94 for Heart disease, 3.77 for Heart attack prediction, and 4.15 for Sonar). Besides, it enhances the processing speed by reducing the processing time by an average rate: 8.7% (28.96 for Diabetes, 1.07 for Heart disease, 3.31 for Heart attack prediction, and 1.4 for Sonar). |
| Databáze: | OpenAIRE |
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