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

• Published in: Biomedical signal processing and control Vol. 94; p. 106247
• Main Authors: Zamani, Abu Sarwar, Hashim, Aisha Hassan Abdalla, Shatat, Abdallah Saleh Ali, Akhtar, Md. Mobin, Rizwanullah, Mohammed, Mohamed, Sara Saadeldeen Ibrahim
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-08-2024
• Subjects: Big Data; Fuzzy Classifier; Health Informatics; Hybrid Flower Pollination Bumblebees Optimization Algorithm; K-Nearest Neighbour; Machine Learning Techniques; Neural Networks; Prediction Model; Fuzzy Classifier; Prediction Model; K-Nearest Neighbour; Hybrid Flower Pollination Bumblebees Optimization Algorithm; Big Data; Machine Learning Techniques; Neural Networks; Health Informatics
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2024.106247

•To implement an effectual healthcare data prediction model with big data.•To develop a Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA).•To optimize the parameters of NN, Fuzzy, KNN with the help of HFPBOA algorithm.•To adopt a designed HFPBOA algorithm to improve the accuracy and precision values.•To adopt an ensemble-based prediction model with the fusion of NN, fuzzy, and KNN.
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 and effective opportunities. This is helpful in providing early prevention, prediction, and detection of disease, thus helping in the enhancement of the overall life quality of the individuals. Likewise, in this paper, a machine learning-based big data analytics model is developed for predicting multi-diseases to provide a better decision support system for various healthcare applications. This developed framework utilizes the MapReduce framework, where the map phase performs feature extraction and the reduce phase performs feature selection for the purpose of handling and processing big data. The required healthcare data is collected from external web sources. In the map phase, the statistical features and the Principal Component Analysis (PCA) features are extracted. In the reduction phase, the optimal features are selected with the aid of the developed Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA). Then, the Ensemble Learning (EL) model is developed to predict the multi-diseases. Moreover, the parameters present in the EL classifiers are optimized by using the same HFPBOA. The final prediction output is obtained by averaging the weight function between the outputs of the NN, KNN, and fuzzy classifier. Thus, the offered 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. The effectiveness computed for the developed multi-disease prediction framework is guaranteed by comparing the results among the recently developed prediction approaches.