Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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Big data analytics for preventive medicine

Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.

A Recent Survey on Internet of Things (IoT) Communication Protocols

A survey on applying machine learning techniques for management of diseases

In this paper, we introduce a novel hybrid method that uses a genetic algorithm (GA) in combination with a swarm optimization algorithm (particle swarm optimization (PSO) or fruit fly optimization algorithm (FOA)) for medical diagnosis. The proposed approaches, called GAPSO-FS and GAFOA-FS, simultaneously employ the genetic algorithm (GA) to choose the optimal feature subset and the swarm optimization algorithms (PSO/FOA) to optimize the SVM parameters. This procedure primarily comprises three synchronized parallel layers, including two optimization layers and an intermediate layer. The intermediate layer is mainly responsible for harmonizing the information from the two optimization layers and then distributing the processed information back to those layers. The major contribution of the proposed approaches is that they fully exploit the advantages of the different algorithms. The genetic algorithm (GA) excels at selecting the optimal feature subset, whereas swarm optimization algorithms (PSO/FOA) are optimal for searching the most appropriate continuous variables, including the penalty parameter C and the hyperplane parameter. We performed several groups of experiments on real-world medical cases from the UCI machine learning data repository to compare our hybrid approaches with well-known optimization techniques. The empirical results demonstrated that the proposed GAPSO-FS and GAFOA-FS can select the best SVM model parameters and a more highly relevant feature subset for the SVM classifier than a single algorithm can, thus improving the classification performance when solving a medical diagnosis problem. Therefore, the proposed approach has potential as a useful tool in medical diagnosis.

Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/cmu2.12112

Metaheuristic-based optimal 3D positioning of UAVs forming aerial mesh network to provide emergency communication services

The main objective of the proposed methodology is to classify an ECG signal as normal or abnormal using the optimal neuro-fuzzy classifier. The proposed work consists of two phases namely, feature extraction and neuro-fuzzy classifier based classification. The beat signals are initially taken from the physio-bank ATM. Then, three types of features are extracted from each signal namely, Morphological-based features, Haar wavelet-based features, and Tri-spectrum based features. After feature extraction, the optimal neuro-fuzzy classifier is classifying the beat signal as normal or abnormal. Here, Artificial Bee Colony (ABC) algorithm is combined with Genetic Algorithm (GA) for training the neuro-fuzzy classifier. For experimental evaluation, the MIT-BIH Arrhythmia Database is utilized and the performances are analyzed in terms of accuracy, sensitivity, and specificity. The experimental results clearly demonstrated that the proposed technique outperformed by having better accuracy of 93% when compared existing technique achieved 84% only.

GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification

Clinical decision making is a complex task for physicians as it requires utmost accuracy of diagnosis. This paper proposes a medical decision support system based on least square support vector machine LS-SVM and simulated annealing SA heuristic for the diagnosis of diabetes in the Pima Indian diabetes PID dataset of the UCI machine learning repository. Fisher score FS algorithm is used to select the most significant features from the given feature set. LS-SVM with radial basis function RBF is used for classification and the SA for optimisation of the kernel parameters of the LS-SVM. The performance of the proposed system is analysed in terms of classification accuracy, sensitivity and specificity using ten-fold cross-validation and confusion matrix. The results show that the classification accuracy of the proposed system outperforms that of various existing systems.

An expert system based on LS-SVM and simulated annealing for the diagnosis of diabetes disease

In this paper, techniques to improve the overall efficiency of an image encoding algorithm that is specifically designed to be used in Wireless Sensor Networks (WSN) are proposed. The inherent nature of WSN, which contains numerous network nodes each having its computational resources, makes the algorithm is being underutilized due to its sequential nature and hence there is a scope to improve speedup by applying the concepts of parallelization on it. The other avenues which are pertinent to the task are the usage of GPU for computation of large arrays as well as the concept of cluster computing to run the algorithm on a cluster of nodes. Another point of importance for such an algorithm is the utilization of memory and how to make its usage more efficient. Hence, the concept of pre-allocation is also applied to the algorithm to make it run faster. An in-depth study of the algorithm is conducted by considering various parameters such as the size and complexity of the key, histogram analysis, correlation values, etc.

Parallelizing Image Encryption Algorithm for Wireless Sensor Networks

A Novel Approach for Web Usage Prediction and Recommendation Using Web Session Clustering

The use of machine learning tools in medical diagnosis is growing progressively. This is mainly because the use of recognition and classification systems has improved in a great deal to help medical experts in diagnosing diseases. Such disease is called hepatitis. Hepatitis disease diagnosis was conducted using KNearest Neighbor (KNN) classifier. The proposed system includes two modules: the feature extraction module and the predictor module. In the feature extraction module, rough set theory is used to preprocess the attributes on condition that the important information is not lost, delete redundant attributes. KNearest Neighbor (KNN) classifier is used to classify the given data’s. Experiments have been conducted on a widely used Hepatitis dataset taken from University of California Irvine (UCI) machine learning repository dataset. The experimental results show that the proposed system can improve the rate of correct diagnosis. The proposed classifier with rough set-based feature selection achieves 84.52 % of accuracy. Different performance metrics are used to show the effectiveness of the proposed system.

S. Anto

Papers

GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification

An expert system based on LS-SVM and simulated annealing for the diagnosis of diabetes disease

Parallelizing Image Encryption Algorithm for Wireless Sensor Networks

A Novel Approach for Web Usage Prediction and Recommendation Using Web Session Clustering

Disease diagnosis using rough set based feature selection and K-nearest neighbor classifier