Data Mining Practical Machine Learning Tools and Techniques

Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO - MLP and 97% for APGWO - MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

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A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.

Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis

Recent technologies have made the life of people more comfortable and more straightforward than it was before With the development of information technology, the Internet of Things (IoT) as an emerging technology has been entered into a lane of development With the advent of IoT, data sharing, and connections among systems, devices, and people have been facilitated, and daily devices have been equipped with sensors and applications to provide their functionality through services As a matter of fact, IoT provides a platform where everyday objects become smarter than before, everyday communication becomes informative, and everyday processes become intelligent In this regard, to provide novel IoT services, numerous heterogeneous frameworks and protocols have been proposed Since the number of IoT devices or objects is growing day by day, and the number of services is also increasing, discovering and locating appropriate services becomes a vital challenge, and the traditional service discovery strategies are not efficient enough to handle this issue Service discovery refers to the process of finding suitable services according to clients' requests Although the service discovery problem has essential impacts on the IoT, there is not any detailed and systematic study of the existing methods in this field Therefore, this paper aims to find, categorize, and investigate all the effective and valid papers in the field of service discovery in the IoT using a systematic method The selected papers are discussed based on various service discovery metrics and other criteria such as adopted architecture, search method, service description, discovery scope, adopted simulation tools, and datasets The advantages and weaknesses of each reviewed paper are specified Moreover, an abreast comparison of the selected papers is presented, in which the aforementioned methods are evaluated considering the mentioned metrics and criteria Finally, future research directions and challenging problems are outlined to help researchers improve their innovations

Service discovery in the Internet of Things: review of current trends and research challenges

Diabetes is one of the fastest growing chronic life threatening diseases that have already affected 422 million people worldwide according to the report of World Health Organization (WHO), in 2018. Due to the presence of a relatively long asymptomatic phase, early detection of diabetes is always desired for a clinically meaningful outcome. Around 50% of all people suffering from diabetes are undiagnosed because of its long-term asymptomatic phase. The early diagnosis of diabetes is only possible by proper assessment of both common and less common sign symptoms, which could be found in different phases from disease initiation up to diagnosis. Data mining classification techniques have been well accepted by researchers for risk prediction model of the disease. To predict the likelihood of having diabetes requires a dataset, which contains the data of newly diabetic or would be diabetic patient. In this work, we have used such a dataset of 520 instances, which has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh. We have analyzed the dataset with Naive Bayes Algorithm, Logistic Regression Algorithm, and Random Forest Algorithm and after applying tenfold Cross- Validation and Percentage Split evaluation techniques, Random forest has been found having best accuracy on this dataset. Finally, a commonly accessible, user-friendly tool for the end user to check the risk of having diabetes from assessing the symptoms and useful tips to control over the risk factors has been proposed.

Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques

Cyber-Physical Systems (CPS) embed computation and communication capability into its core to regulate physical processes and seamlessly mediate between the cyber and the physical world for various control and monitoring tasks. Health CPS, a variant of CPS in the healthcare sector, acts as a health monitoring system to dynamically capture, process, and analyze health sensor data through integrated internet of things (IoT)-enabled cyber-physical processes. These systems can suitably support patients suffering from non-communicable diseases (NCDs) or who are at risk of suffering from those. Identifying the risk of NCDs, such as heart disease and diabetes, requires artificial intelligence (AI) techniques into the core of health CPS. Recently, there has been growing interest to incorporate machine learning into CPS, which can facilitate the disease classification, detection, monitoring, and prediction of several NCDs. However, there is a shortage of visible work that focus on early-stage risk prediction of these diseases. In this work, we propose a novel machine learning based health CPS framework that addresses the challenge of effectively processing the wearable IoT sensor data for early risk prediction of diabetes as an example of NCDs. In the experiment, a verified diabetic dataset has been used for training, while the testing has been performed on an artificially generated data collection from sensors. The experiment with several machine learning algorithms shows the effectiveness of the proposed approach in achieving the maximum precision from the Random Tree algorithm, which requires a minimum time of 0.01s to construct a model and obtains 94% accuracy to predict the probability of diabetes at an early point.

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Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

Knowledge-driven machine learning based framework for early-stage disease risk prediction in edge environment

Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.

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A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

In the Internet of things (IoT), research arena Constrained application protocol (CoAP) is becoming the most accepted protocol for IoT service discovery. To make the protocol more performance promising new recommendations are amending to the existing mechanism of discovering services. In this paper we propose, LOAMY, a cloud-based middleware, aiming at providing mobility and interoperability for the dynamically changing context of services in constrained IoT network. Our main objective is to combine the advantageous characteristics of existing CoAP Centralized and Distributed models into a single model for an efficient service discovery in the context of constrained IoT Network. Both the existing centralized and distributed models of CoAP has been simulated using Contiki OS and Cooja simulator to build the experimental setup. We have conducted detailed experiments for our proposed model and compared the results with existing centralized and distributed CoAP service discovery mechanisms to demonstrate the effectiveness of our proposed method. We have considered heterogeneous types of client requests for the experimental analysis.

Rahatara Ferdousi

Papers

Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques

Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS

Knowledge-driven machine learning based framework for early-stage disease risk prediction in edge environment

A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

LOAMY: a Cloud-based Middleware for CoAP-based IoT Service Discovery