The innovative technologies emerged with the industrial revolution “Industry 4.0” as well as the new ones on the way of advanced digitalization enable delivering enhanced, value-added and cost-effective manufacturing and service operations. One of the first areas of focus for Industry 4.0 applications is operations related to healthcare services. Effective management of healthcare resources, clinical care processes, service planning, delivery and evaluation of healthcare operations are essential for a well-functioning healthcare system. Yet, with the adoption of technologies such as Internet of Health Things, Medical Cyber–Physical Systems, Machine Learning, and Big Data (BD), the healthcare sector has recognized the relevance of Industry 4.0. The concept of BD offered numerous advantages and opportunities in this field. It changed the way information is gathered, shared and utilized. Hence, in this study our main ambition is to provide readers with a review of publications which lie within the intersection of Industry 4.0, BD, and healthcare operations and give future perspectives. Our review shows that BD constitutes an important place on the technologies Industry 4.0 provides in the healthcare domain. It helps design, improve, analyze, assess and optimize operations in the domain. 

Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives

A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit

In Indian economy, agriculture is the prime vocation that avails in the overall development of the country. Tamil Nadu occupies approximately 7% of the nation's population, with 3% of water resources and 4% of land resources at the country level. The crop suitability prediction is of prime importance to enhance the nutritional security to the developing country. Based on several crops grown in a particular place, and the availability of natural resources, one can identify the suitability of crops that can be grown in a particular place. To this end, many mathematical tools were developed, but they failed to include processing of uncertainties present in the accumulated data. Therefore, in this paper an effort has been made to process the uncertainties by hybridizing rough set on fuzzy approximation space and neural network. The rough set on fuzzy approximation space identifies the almost indiscernibility among the natural resources and helps in minimizing the computational procedure on employing data reduction techniques, whereas neural network helps in prediction process. The proposed model is analysed on agriculture data of Vellore District of Tamil Nadu, India, and achieved 93% of classification accuracy in validation. The model is compared with an earlier model and achieved 8% more accuracy while predicting unseen associations.

Crop suitability prediction in Vellore District using rough set on fuzzy approximation space and neural network

Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.

Neural network and rough set hybrid scheme for prediction of missing associations

Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

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Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

Healthcare applications produce a large amount of data from multiple heterogeneous healthcare devices. These heterogeneous devices produce data in different formats. In clinical decisions, generally, one source of information may not be able to achieve an accurate decision. For accurate analysis, heterogeneous data can be fused, which in turn helps to develop a more understanding of a particular disease. Fusing data from diverse sources like clinical repositories, sensory devices, historical or textual data is vital for both patients and healthcare providers. However, with the increasing number of sensory devices, the complexity of data fusion is also increasing. Various issues like complex distributed processing, unreliable data communication, the uncertainty of data analysis, data transmission at different rates have identified. This paper aims to present different perspectives of data fusion to evaluate healthcare applications based on these issues.Further, these proposed different perceptual classifications are applied to assess applications in the healthcare domain. We highlight the challenges of data fusion in the healthcare domain and discuss future research opportunities for data fusion in healthcare. We also offer a brief introduction to data fusion and machine learning by discussing some of the applications and typical techniques.

Clinical Data Fusion and Machine Learning Techniques for Smart Healthcare

The information technology revolution has brought drastic change in the way data is collected or generated for decision mining. The accumulated data has no relevance unless it provides certain useful information pertaining to the interest of an organization. The real challenge lies in converting high dimensional data into knowledge and to use this knowledge for the development of the organization. On the other hand, hiding an organization’s sensitive information is a major concern. Much research has been carried out in this direction. This chapter discusses various privacy preservation techniques that can be employed in an information system to safeguard the sensitive information of an organization. This chapter also highlights sensitive fuzzy association rules that can be generated from an information system. The authors provide illustrations wherever necessary to give a clear idea of the concepts developed.

Privacy Preservation in Information System

Data mining is an emerging technology where researchers explore innovative ideas in different domains, particularly detecting anomalies. Instances in the dataset which considerably deviate from others by their common patterns are known as anomalies. The state of being ambiguous and not affording certainty of data exists in this world of nature. Rough Set Theory is a proven methodology which deals with ambiguity and uncertainty of data. Research works that have been done until this point were focused on numeric or categorical type, which fails when the attributes are mixed type. By using fuzzy proximity and ordering relations, the numerical data has been converted to categorical data. This article presented an idea for detecting outliers in mixed data where the weighted density values of attributes and objects are calculated. The proposed approach has been compared with existing outlier detection methods by taking the hiring dataset as an example and benchmarked with Harvard dataverse datasets to prove its efficiency and performance.

A. Geetha Mary

Papers

Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

Clinical Data Fusion and Machine Learning Techniques for Smart Healthcare

Privacy Preservation in Information System

A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method