Bibliometric of Feature Selection Using Optimization Techniques in Healthcare using Scopus and Web of Science Databases

Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is built using the feature selection algorithm. The solitary motive of the research paper analysis is to comprehend the reach and utility of optimization algorithms such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO) in the field of Health care. The aim is to bring efficiency and maximum optimization in the health care sector using the vast information that is already available related to these fields. With the help of data sets that are available in the health care analysis, our focus is to extract the most important features using optimization techniques and work on different algorithms so as to get the most optimized result. Precision largely depends on usefulness of features that are taken into consideration along with finding useful patterns in those features to characterize the main problem. The Performance of the optimized algorithm finds the overall optimum with less function evaluation. The principle target of this examination is to optimize feature selection technique to bring an optimized and efficient model to cater to various health issues. In this research paper, to do bibliometric analysis Scopus and Web of Science databases are used. This bibliometric analysis considers important keywords, datasets, significance of the considered research papers. It also gives details about types, sources of publications, yearly publication trends, significant countries from Scopus and Web of Science. Also, it captures details about co-appearing keywords, authors, source titles through networked diagrams. In a way, this research paper can be useful to researchers who want to contribute in the area of feature selection and optimization in healthcare. From this research paper it is observed that there is a lot scope for research for the considered research area. This kind of research will also be helpful for analyzing pandemic scenarios like COVID-19

CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data

A comprehensive study on disease risk predictions in machine learning

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

Objectives In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.Methods We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review ", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. The review was registered on PROSPERO.ResultsFrom a total of 648 studies initially retrieved, 68 articles met the inclusion criteria.Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.Conclusions Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice

Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease

This study aims to improve the accuracy of the classification algorithm for diagnosing chronic kidney disease. There are several models of data mining. In classification, the Support Vector Machine (SVM) algorithm is widely used by researchers worldwide. The data used is a chronic kidney disease dataset taken from the UCI machine learning repository. This data consists of 25 attributes and 11 numeric data attributes, and 14 negative attributes. To call continuously, discrete data is used. Meanwhile, data is selected using Correlation-based Feature Selection (CFS) to reduce irrelevant and redundant data. The research results by applying discretization and feature selection based on correlation for classification in the SVM algorithm with 10-fold cross-validation show an increase in accuracy of 0.5%. The classification of the vector machine support algorithm in the diagnosis of chronic kidney disease produces an accuracy of 99.25%, and after applying discretization and correlation-based feature selection, produces an accuracy of 99.75%. Implementation of discretion and correlation-based feature selection to optimize support vector machine for diagnosis of chronic kidney disease has increased accuracy by 0.5%. The proposed method is feasible as a method of diagnosing chronic kidney disease

Edge-Based Health Care Monitoring System: Ensemble of Classifier Based Model

Health Monitoring System (HMS) is an excellent tool that actually saves lives. It makes use of transmitters to gather information and transmits it wirelessly to a receiver. Essentially, it is much more practical than the large equipment that the majority of hospitals now employ and continuously checks a patient's health data 24/7. The primary goal of this research is to develop a three-layered Ensemble of Classifier model on Edge based Healthcare Monitoring System (ECEHMS) and Gauss Iterated Pelican Optimization Algorithm (GIPOA) including data collection layer, data analytics layer, and presentation layer. As per our ECEHMS-GIPOA, the healthcare dataset is collected from the UCI repository. The data analytics layer performs preprocessing, feature extraction, dimensionality reduction and classification. Data normalization will be done in preprocessing step. Statistical features (Min/Max, SD, Mean, Median), improved higher order statistical features (Skewness, Kurtosis, Entropy), and Technical indicator based features were extracted during Feature Extraction step. Improved Fuzzy C-means clustering (FCM) will be used for handling the Dimensionality reduction issue by clustering the appropriate feature set from the extracted features. Ensemble model is introduced to predict the disease stage that including the models like Deep Maxout Network (DMN), Improved Deep Belief Network (IDBN), and Recurrent Neural Network (RNN). Also, the enhancement in prediction/classification accuracy is assured via optimal training. For which, a GIPOA is introduced. Finally, ECEHMS-GIPOA performance is compared with other conventional approaches like ASO, BWO, SLO, SSO, FPA, and POA