Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics

Zamani, Abu Sarwar and Hassan Abdalla Hashim, Aisha and Shatat, Abdallah Saleh Ali and Akhtar, Md. Mobin and Rizwanullah, Mohammed and Mohamed, Sara Saadeldeen Ibrahim (2024) Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics. Biomedical Signal Processing and Control, 94. pp. 1-5. ISSN 1746-8094 E-ISSN 1746-8108

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Abstract

As a result of the availability of healthcare data in sheer size, big data analytics has to grow regularly in this industry to ensure new andeffective opportunities. This is helpful in providing early prevention, prediction, and detection of disease, thus helping in the enhancement ofthe overall life quality of the individuals. Likewise, in this paper, a machine learning-based big data analytics model is developed for predictingmulti-diseases to provide a better decision support system for various healthcare applications. This developed framework utilizes theMapReduce framework, where the map phase performs feature extraction and the reduce phase performs feature selection for the purpose ofhandling and processing big data. The required healthcare data is collected from external web sources. In the map phase, the statisticalfeatures and the Principal Component Analysis (PCA) features are extracted. In the reduction phase, the optimal features are selected with theaid of the developed Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA). Then, the Ensemble Learning (EL) model isdeveloped to predict the multi-diseases. Moreover, the parameters present in the EL classifiers are optimized by using the same HFPBOA. Thefinal prediction output is obtained by averaging the weight function between the outputs of the NN, KNN, and fuzzy classifier. Thus, theoffered model attains 40.1%, 28.7%, 23.6%, and 10.5% improved than SSA-EL, DOA-EL, BOA-EL, and FA-EL respectively in terms of best value. Theeffectiveness computed for the developed multi-disease prediction framework is guaranteed by comparing the results among the recentlydeveloped prediction approaches.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Big Data; Fuzzy Classifier; Health Informatics; Hybrid Flower Pollination Bumblebees Optimization Algorithm; K-Nearest Neighbour; Machine Learning Techniques; Neural Networks; Prediction Model
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Aisha Hassan Abdalla Hashim
Date Deposited:	29 May 2024 08:31
Last Modified:	11 Jul 2024 09:35
URI:	http://irep.iium.edu.my/id/eprint/112311

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