The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges

SummaryIn recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. Thes...

https://journals.sagepub.com/doi/pdf/10.1177/0141076818815510

Artificial intelligence-enabled healthcare delivery.

Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care.

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A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection

At present days, world is facing several issues like irregular distribution of medicinal resources, new chronic diseases, and the raising operating cost. The way of combining recent technologies into the medical system will helps to significantly resolve the problems. This study introduces a big health application system based on optimal artificial neural network (OANN) for heart disease diagnosis, which is considered as a deadliest disease in all over the globe. The proposed OANN includes a set of two main processes namely, distance based misclassified instance removal (DBMIR) and teaching and learning based optimization (TLBO) algorithm for ANN, called (TLBO-ANN). The proposed model is developed using a Big Data framework like Apache Spark. The presented OANN model operates on two phases, namely offline prediction and online prediction. During the offline prediction stage, the benchmark heart disease dataset will be used to train a model and performs testing. Similarly, at the online prediction stage, the real time data will be streamed into Apache Spark model and the filtered data will be diagnosed by the use of trained model to obtain the prediction results. The performance of the presented OANN model has been tested using a benchmark heart disease dataset from UCI repository. A comprehensive experimental result analysis clearly verified the better outcome of the OANN model over the compared methods. The proposed method is found to be an effective tool to analyze big data based heart disease prediction model to satisfy the need of increasing number of heart patients.

An optimal artificial neural network based big data application for heart disease diagnosis and classification model

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.

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Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia

https://ejmcm.com/article_1159_94644ab0d4188b17794adf0be9b28238.pdf

PSO-ANN based diagnostic model for the early detection of dengue disease

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches Soft computing approaches have the ability to adapt itself according to problem domain Another aspect is a good balance between exploration and exploitation processes These aspects make soft computing approaches more powerful, reliable and efficient The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases Second objective is to identify various diseases for which these approaches are applied Third objective is to categories the soft computing approaches for clinical support system In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc A detailed discussion on these approaches are presented in literature section This work summarizes various soft computing approaches used in healthcare domain in last one decade These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito (principally, Aedesaegypti). Symptoms of the dengue fever mimic those of a number of other infectious and/or mosquito-borne tropical diseases such as Viral flu, Chikungunya, and Zika fever. Yet, with dengue fever, human life can be more at risk due to severe depletion of blood platelets. Thus, early detection of dengue disease can ensure saving lives; furthermore, it can help in making a preventive move before the disease progresses to epidemic proportion. Hence, the target of this article is to propose a model for an early detection and precise diagnosis of dengue disease. In this article, three prevalent machine learning methodologies, including, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB) are evaluated for designing a diagnostic model. The performance of these models is assessed utilizing available dengue datasets. Results comparing and contrasting performance of diagnostic models utilizing accuracy, sensitivity, specificity and error rate parameters showed that ANN-based diagnostic model appears to yield better performance measures over both the DT and NB models.

The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches

Classification schemes have been applied in the medical arena to explore patients' data and extract a predictive model.This model helps doctors to improve their prognosis, diagnosis, or treatment planning processes.The aim of this work is to utilize and compare different decision tree classifiers for early diagnosis of Dengue. Six approaches, mainly J48 tree, random tree, REP tree, SOM, logistic regression, and naïve Bayes, have been utilized to study real-world Dengue data collected from different hospitals in the Delhi, India region during 2015-2016. Standard statistical metrics are used to assess the efficiency of the proposed Dengue disease diagnostic system, and the outcomes showed that REP tree is best among these classifiers with 82.7% efficient in supplying an exact diagnosis.

Shalini Gambhir

Papers

PSO-ANN based diagnostic model for the early detection of dengue disease

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

The Diagnosis of Dengue Disease: An Evaluation of Three Machine Learning Approaches

Early Diagnostics Model for Dengue Disease Using Decision Tree-Based Approaches