There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. The proposed method was compared with the standalone ELM, hybrid of ELM-PSO, and binary hybrid PSOGSA (hybrid of PSO with gravitational search algorithm) methods. Monthly precipitation and runoff data were used as inputs to the models to examine their accuracy in terms of different statistical indexes. Test results showed that the proposed ELM-PSOGWO provided more accurate results than the standalone ELM, hybrid ELM-PSO, ELM-GWO nd binary hybrid PSOGSA methods in monthly runoff prediction. ELM-PSOGWO reduced the RMSE in prediction of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA by 38.2, 22.8, 22.4 and 16.7%, respectively. The PSO and GWO based ELM models also performed better than standalone ELM models, with an improvement in RMSE by 19.9 to 20.3%, respectively. Results also showed that adding precipitation as input enhanced the prediction accuracy of models. ELM-PSOGWO was also able to provide more precise estimates of peak runoff with the lowest absolute mean relative error compared to other methods. The results indicate the potential of ELM-PSOGWO model to be recommended for monthly runoff prediction.

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson’s (PD) disease has become increasingly important The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson’s disease prediction The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model Moreover, the proposed ANFIS+PSOGWO applied for Parkinson’s disease prediction and achieved an accuracy of 875% The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson’s disease prediction The proposed model produced accuracy for the Parkinson’s disease prediction has outperformed its closest competitors in all algorithms by 73%

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Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.

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An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Abstract Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop machine learning. Depending on who is in control of the learning process, we can identify: active learning, in which the system remains in control; interactive machine learning, in which there is a closer interaction between users and learning systems; and machine teaching, where human domain experts have control over the learning process. Aside from control, humans can also be involved in the learning process in other ways. In curriculum learning human domain experts try to impose some structure on the examples presented to improve the learning; in explainable AI the focus is on the ability of the model to explain to humans why a given solution was chosen. This collaboration between AI models and humans should not be limited only to the learning process; if we go further, we can see other terms that arise such as Usable and Useful AI. In this paper we review the state of the art of the techniques involved in the new forms of relationship between humans and ML algorithms. Our contribution is not merely listing the different approaches, but to provide definitions clarifying confusing, varied and sometimes contradictory terms; to elucidate and determine the boundaries between the different methods; and to correlate all the techniques searching for the connections and influences between them. 

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Human-in-the-loop machine learning: a state of the art

Although there are many attempts to build an optimal model for feature selection in Big Data applications, the complex nature of processing such kind of data makes it still a big challenge. Accordingly, the data mining process may be obstructed due to the high dimensionality and complexity of huge data sets. For the most informative features and classification accuracy optimization, the feature selection process constitutes a mandatory pre-processing phase to reduce dataset dimensionality. The exhaustive search for the relevant features is time-consuming. In this paper, a new binary variant of the wrapper feature selection grey wolf optimization and particle swarm optimization is proposed. The K-nearest neighbor classifier with Euclidean separation matrices is used to find the optimal solutions. A tent chaotic map helps in avoiding the algorithm from locked to the local optima problem. The sigmoid function employed for converting the search space from a continuous vector to a binary one to be suitable to the problem of feature selection. Cross-validation K-fold is used to overcome the overfitting issue. A variety of comparisons have been made with well-known and common algorithms, such as the particle swarm optimization algorithm, and the grey wolf optimization algorithm. Twenty datasets are used for the experiments, and statistical analyses are conducted to approve the performance and the effectiveness and of the proposed model with measures like selected features ratio, classification accuracy, and computation time. The cumulative features picked through the twenty datasets were 196 out of 773, as opposed to 393 and 336 in the GWO and the PSO, respectively. The overall accuracy is 90% relative to other algorithms ' 81.6 and 86.8. The total processing time for all datasets equals 184.3 seconds, wherein GWO and PSO equal 272 and 245.6, respectively.

Improved Feature Selection Model for Big Data Analytics

The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.

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Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

Internet of Things (IoT) emerged as one of the leading technological advancements of our days. IoT generates enormous quantities of valuable data that need on time processing, resulting in...

Leveraging mist and fog for big data analytics in IoT environment

Nowadays, Healthcare is one of the most critical issues that need efficient and effective analysis. Data mining provides many techniques and tools that help in getting a good analysis for healthcare data. Data classification is a form of data analysis for deducting models. Mining on a reduced version of data or a lower number of attributes increases the efficiency of system providing almost the same results. In this paper, a comparative study between different data reduction techniques is introduced. Such comparison is tested against classification algorithms accuracy. The results showed that fuzzy rough feature selection outperforms rough set attribute selection, gain ratio, correlation feature selection and principal components analysis. General Terms Data mining, bioinformatics

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Ibrahim M. El-Hasnony

Papers

Improved Feature Selection Model for Big Data Analytics

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

Leveraging mist and fog for big data analytics in IoT environment

Comparative Study among Data Reduction Techniques over Classification Accuracy