Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data

Vidhya, K.; Shanmugalakshmi, R.

doi:10.1007/s11227-019-03132-w

Cited by 24 publications

(16 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method uses the linear discriminant analysis (LDA)+ANFIS for dew space classification to detect DENF and quickly track the likelihood of coronary heart disease (CHD) of users affected by the DENF. Vidhya and Shanmugalakshmi [40] examined multiple diseases using a modified adaptive neuro-fuzzy inference system (M-ANFIS). Data integration and features extraction are used for data preprocessing.…”

Section: B Iot Big Data Analytics Infrastrucuresmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

View full text Add to dashboard Cite

Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson's (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson's disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson's disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson's disease prediction. The proposed model produced accuracy for the Parkinson's disease prediction has outperformed its closest competitors in all algorithms by 7.3%.

show abstract

Section: B Iot Big Data Analytics Infrastrucuresmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

View full text Add to dashboard Cite

show abstract

“…Vidhya and Shanmugalakshmi [14] developed a Modified adaptive neuro-fuzzy inference system (M-ANFIS) for analyzing the multi-disease healthcare Big Data (BD). The health care domain obtained an influence based on the BD that affects the data sources as they are concerned with healthcare organization as it is famous with the volume, complexity, high dynamism, and heterogeneity.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical Big Data Analysis using LSTM based Co-Learning Model with Whale Optimization Approach

2022

IJIES

View full text Add to dashboard Cite

The medical and health service has become advanced and the smart health care platform has made the diagnosis more robust for the treatment. The accurate analysis of medical data is dependent on early disease detection and the value of accuracy is reduced when the medical data quality is poor. However, the existing approaches failed to deploy the learning model to handle the heterogeneous medical data. The present research work has used the machine learning algorithm effectively for the chronic disease prediction such as heart disease, cancer, diabetes, stroke, and arthritis for the frequent communities and the medical data are available from the disparate sources including a wide variety of information has made difficult to analyse and retrieve. The detailed information about the attributes is required to be known as it is significant in analyzing the medical data. The process of selecting the attributes plays an important role in decision-making for medical disease analysis. The proposed model was tested for its effectiveness and was validated with various benchmark data which was collected from distinct domains. The present research work utilizes Spark Streaming layers for data streaming to diagnose further based on Long Short Term Memory (LSTM) Co-learning with whale optimization approach is from the heterogeneous medical data. The results obtained by the proposed method analysed the disease abnormality better. The existing model obtained SVM-RBF of 81.30 %, k-fold cross-validation and hyperparameters tuning with Random Forest of 94.9%, CNN of 90%, and the proposed LSTM based Co-learning model with Whale optimization approach obtained 98.6 % which is better compared to the existing models.

show abstract

“…y T+1 j = y T j + y T+1 NEW,i j = 1, 2, ..M (33) At time T + 1, the weighted solution of the ith individual is referred as i and at time T, y T j is represented as the solution of the jth individual. The Algorithm 3 describes the working function of the reformative teaching-learning based deep neural network (RTL-DNN).…”

Section: Multi-disease Analysis Using Reformative Teacher Learning Ba...mentioning

confidence: 99%

Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Parimanam

Lakshmanan

Palaniswamy

2022

Concurrency and Computation

View full text Add to dashboard Cite

With the rapid development of health information systems in recent years covering big data trends, clinical information is being adapted to understand important health trends and provide timely prophylactic treatment. However, the current plan fails to maintain a highly organized analysis and processing of health data. In addition, it collects issues that play an important role in determining quality. In this article, we propose a hybrid optimization based learning technique for multi‐disease analytics (HOL‐MD) from healthcare big data using optimal pre‐processing, clustering, and classifier. First, we introduce a capuchin search based optimization algorithm for preprocessing which removes the unwanted artifacts to enhance the detection accuracy. Second, we introduce a modified Harris Hawks optimization based clustering (MHHOC) technique used to select optimal features among multiple features which discovers the subgroups and reduce the dimensionality issues. We evaluated the performance of proposed HOL‐MD technique using standard US healthcare organization dataset, SUSY and HIGGS datasets. Finally, we proved the effectiveness of proposed RTL‐DNN techniques in terms of 11.5%, 9.3%, 2.75%, and 3.45% higher than the existing state‐of‐art techniques in terms of accuracy, precision, recall, and F‐measure respectively.

show abstract

Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data

Cited by 24 publications

References 17 publications

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Medical Big Data Analysis using LSTM based Co-Learning Model with Whale Optimization Approach

Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Contact Info

Product

Resources

About