A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS
Open accessAbstract
The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few. Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT). The IoMT has an important role to play in reducing the mortality rate by the early detection of disease. The prediction of heart disease is a key issue in the analysis of clinical dataset. The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques. Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low. Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed. The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm. The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima. The learning parameters are optimized utilizing MSSO to provide better results for ANFIS. The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc. The heart condition is identified by classifying the received sensor data using MSSO-ANFIS. A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction. The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches. The proposed MSSO-ANFIS prediction model obtains an accuracy of 99.45 with a precision of 96.54, which is higher than the other approaches.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes an IoMT-based heart disease diagnosis framework that uses a Levy-based crow search algorithm for feature selection and a modified salp swarm optimization–optimized ANFIS (MSSO-ANFIS) for classification. It reports 99.45% accuracy, 96.54 precision, and 97.89 sensitivity, outperforming several compared methods.
Key findings
- MSSO-ANFIS achieved an average accuracy of 99.45% versus 81.2% for conventional ANFIS, with precision 96.54 and sensitivity 97.89.
- On the test set, MSSO-ANFIS correctly classified 3190 of 3200 heart disease patients and 1760 of 1800 patients without heart disease; classification error was 0.55.
- ROC analysis gave AUC 0.86 for the training dataset and 0.99 for the test dataset.
- The proposed LCSA feature-selection method produced higher fitness values than conventional CSA across iterations (e.g., at 25 iterations: 2733.587 vs. 2531.357).
Methods
- Design: an IoMT healthcare monitoring framework for heart disease prediction, with preprocessing, feature selection using Levy-based crow search algorithm (LCSA), and classification using MSSO-ANFIS.
- Data: simulation used the Hungarian and Framingham datasets; the datasets contain around 76 attributes, but 13 important attributes were used.
- Analysis: performance evaluated with sensitivity, specificity, accuracy, precision, F1-score, classification error, confusion matrices, and ROC/AUC, comparing MSSO-ANFIS against HOBDBNN, GA-RFNN, HRFLM, ANN-FuzzyAHP, x²-DNN, logistic regression, ICA with metaheuristic, and hybrid intelligent systems.
Limitations
- The paper states that MSSO and ANFIS have exploration and exploitation ability but suffer from slow and premature convergence, and that the probability distribution keeps varying by generation.
- Evident from the text: evaluation is simulation-based on two datasets, with no real-world IoMT deployment or clinical validation; the authors state future work will realize the approach using wearable technologies and available market products.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 10 cited sources checked · 0 supported · 0 flagged
- Findingown data
The AUC obtained for the proposed MSSO-ANFIS on the training dataset is 0.86.
“The obtained AUC is 0.86 which is close to 1.”
Quote in the paperSubstantial claim - Findingreasoning
The overall running time of MSSO-ANFIS is approximately O(n^2).
“The overall running time of MSSO-ANFIS can be computed as shown in equation ( 26) O(t(d * n + COF * n) + O (1) + O (n) + O n 2 ≈ O n 2 (26”
Quote in the paperNot a substantial claim - Findingown data
The proposed MSSO-ANFIS gives the highest values for the remaining performance measures.
“Likewise, with respect to the remaining measures, the proposed MSSO-ANFIS gives the highest values.”
Quote in the paperSubstantial claim - Findingown data
The proposed MSSO-ANFIS obtains the highest level of accuracy (99.45) compared with the existing methods (HOBDBNN 99.03, GA-RFNN 96.43, HRFLM 88.4, ANN-FuzzyAHP 91.10, x2-DNN 93.33, logistics regression 89, ICA with meta-heuristic 94.03, hybrid intelligent systems 94.05).
“Similarly, in relation to the accuracy of heart disease prediction, the proposed MSSO-ANFIS obtains the highest level of accuracy (99.45) compared with the existing HOBDBNN (99.03),GA-RFNN (96.43),HRFLM (88.4), ANN-FuzzyAHP (91.10), x 2 -DNN(93.33),logistics regression (89), ICA with meta-heuristic (94.03) and hybrid intelligent systems (94.05).”
Quote in the paperSubstantial claim - Findingown data
For iteration values 5, 10, 15, 20, and 25, the proposed LCSA has higher fitness values than the existing CSA.
“For a different number of iteration values, such as 5, 10, 15, 20, and 25, the existing CSA has FVs of 2011.35,2144.325,2234.637,2434.127, and 2531.357,respectively, whereas the proposed LCSA has FVs of 2114.356,2374.956,2436.245,2633.897, and 2733.587,respectively.Hence, this proves that the proposed LCSA technique has a higher FV than CSA.”
Quote in the paperSubstantial claim
- Findingnone given
The proposed LCSA for feature selection achieved the highest fitness values for all iterations.
“The proposed LCSA for feature selection achieved the highest fitness values for all iterations.”
Quote in the paperNot a substantial claim - Findingown data
The proposed technique has better AUC values compared with the existing works [24], [25].
“Table 3 shows that the proposed technique has better AUC values compared with the existing works [24], [25].”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingown data
The proposed MSSO-ANFIS achieves the lowest classification error (0.55) compared with the existing methods (HOBDBNN 0.97, GA-RFNN 3.57, HRFLM 11.6, ANN-FuzzyAHP 8.9, x2-DNN 6.67, logistics regression 11, ICA with meta-heuristic 5.97, hybrid intelligent systems 5.95).
“The proposed MSSO-ANFIS achieves the lowest error (0.55) when compared with the existing HOBDBNN (0.97), GA-RFNN (3.57), HRFLM (11.6), ANN-FuzzyAHP (8.9), x 2 -DNN (6.67), logistics regression (11), ICA with meta-heuristic (5.97) and hybrid intelligent systems (5.95)”
Quote in the paperSubstantial claim - Interpretationreasoning
The learning parameters of ANFIS are optimized using MSSO to provide better results.
“The learning parameters are optimized utilizing MSSO to provide better results for ANFIS.”
- Also stated in: § I. INTRODUCTION · plain
“The learning parameters of ANFIS are optimized using MSSO to provide better results.”
Quote in the paperNot a substantial claim - Also stated in: § I. INTRODUCTION · plain
- Interpretationown data
The MSSO-ANFIS is superior to the other classification techniques in terms of achieving and maintaining a heart patient monitoring system.
“Therefore, we can state that the MSSO-ANFIS is superior than the other classification techniques in terms of achieving and maintaining a heart patient monitoring system.”
Quote in the paperSubstantial claim - Interpretationreasoning
The proposed method uses a Levy-based crow search algorithm to overcome the crow search algorithm's convergence issues.
“To overcome such difficulties, the proposed method uses a Levy-based CSA.”
Quote in the paperSubstantial claim - Interpretationreasoning
The proposed MSSO-ANFIS improves search capability using the Levy flight algorithm.
“The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm.”
- Also stated in: § II. LITERATURE SURVEY · plain
“We modified the salp swarm optimization algorithm to improve the search capability using Levy flight.”
Quote in the paperNot a substantial claim - Also stated in: § II. LITERATURE SURVEY · plain
- Premisecitation
There are fewer studies that use hybrid metaheuristic techniques for heart disease prediction.
“However, there are fewer studies [25], [29], [30] which use hybrid metaheuristic techniques.”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationown data
The proposed MSSO-ANFIS algorithm is superior to existing algorithms.
“The obtained results show that the proposed MSSO-ANFIS algorithm is superior than existing algorithms.”
Quote in the paperSubstantial claim - Interpretationreasoning
The proposed IoMT framework for heart disease diagnosis uses MSSO-ANFIS to improve classifier performance.
“The proposed IoMT framework for the diagnosis of heart disease uses a modified salp swarm optimization and adaptive neuro-fuzzy inference system to improve the performance of classifier.”
Quote in the paperSubstantial claim - Premisecitation
The accuracy of machine learning classifiers for heart disease diagnosis has been found to be low.
“However, the accuracy of such classifiers has been found to be low.”
- Also stated in: § Abstract · plain
“Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low.”
Quote in the paperSubstantial claim0 of 3 sources checked - Also stated in: § Abstract · plain
- Premisecitation
The crow search algorithm does not guarantee convergence due to poor exploration of the search space.
“However, this search strategy does not guarantee convergence due to the poor exploration of the search space [10].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] source
Not checked in the free run.
- May combine two claims. 10
May combine two claims.
Findingown dataIn the proposed MSSO-ANFIS test set, of 3200 heart disease patients, 3190 were correctly classified and 10 misclassified; of 1800 patients without heart disease, 1760 were correctly classified and 40 misclassified.
“In Fig. 5(e), which is obtained from the test set, it can be seen that of the 3200 heart disease patients, 3190 were correctly classified who has the heart disease while 10 were misclassified.Furthermore, of the 1800 patients without heart disease, 1760 were correctly classified while 40 were misclassified.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe proposed MSSO-ANFIS prediction model achieves better accuracy than other approaches, obtaining an accuracy of 99.45 and a precision of 96.54.
“The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches.”
- Also stated in: § Abstract · plain
“The proposed MSSO-ANFIS prediction model obtains an accuracy of 99.45 with a precision of 96.54, which is higher than the other approaches.”
Quote in the paperSubstantial claim- Also stated in: § Abstract · plain
May combine two claims.
Findingown dataThe proposed MSSO-ANFIS algorithm has an average accuracy of 99.45% for patients in both classes, compared with 81.2% accuracy for the conventional ANFIS algorithm.
“The overall performance of the proposed MSSO-ANFIS algorithm has an average accuracy of 99.45% (Fig. 5e) for patients in both classes (Class 0 and Class 1) compared with 81.2% accuracy for the conventional ANFIS algorithm (Fig. 5b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe proposed MSSO-ANFIS gives a sensitivity value of 97.89, which is higher than the other approaches.
“Of these, the existing RFNN obtains the highest value for sensitivity, but the proposed MSSO-ANFIS gives a sensitivity value of 97.89, which is higher than the other approaches.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the proposed MSSO-ANFIS training set, of 3200 heart disease patients (Class 0), 2800 were correctly classified and 400 misclassified.
“From Fig. 4(d), this can be analysed that of the 3200 heart disease patients (Class 0), 2800 were correctly classified as having a heart disease while 400 were misclassified.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the conventional ANFIS training set, of 3200 heart disease patients (Class 0), 2550 were correctly classified and 650 misclassified; of 1800 patients without heart disease (Class 1), 1350 were correctly classified and 450 misclassified.
“In Fig. 5(a), it can be observed that of the 3200 heart disease patients (Class 0), 2550 were correctly classified who have the heart disease while 650 were misclassified.However, of the 1800 patients without heart disease (Class 1), 1350 were correctly classified while 450 were misclassified.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the conventional ANFIS test set, of 3200 heart disease patients, 2720 were correctly classified and 480 misclassified; of 1800 patients without heart disease, 1420 were correctly classified and 380 misclassified.
“In Fig. 5(b), which is obtained from the test set, it can be seen that of the 3200 heart disease patients, 2720 were correctly classified who have the heart disease while 480 were misclassified.Furthermore, of the 1800 patients without heart disease, 1420 were correctly classified while 380 were misclassified.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenThe proposed MSSO-ANFIS technique gives higher values for precision, recall, F1-score, and accuracy and the lowest values for classification error when compared with the existing methods.
“The proposed MSSO-ANFIS technique gives higher values for precision, recall, F1-score, and accuracy and the lowest values for classification error when compared with the existing HOBDBNN, GA-RFNN, HRFLM, ANN-FuzzyAHP, x 2 -DNN, logistics regression, ICA with meta-heuristic and hybrid intelligent systemsmethods.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima.
“Furthermore, the regular learning process in ANFIS is dependent on gradient-based learning which has a tendency to become trapped in local minima [14].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe proposed MSSO-ANFIS works well in accurately detecting the heart condition of patients and continuously monitoring patients' heart conditions.
“From these results, we can state that the proposed MSSO-ANFIS works well in accurately detecting the heart condition of patients and continuously monitoring patients' heart conditions.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima.
“The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima.”
“The regular learning process in ANFIS is dependent on gradient-based learning which has a tendency to become trapped in minima.”
The paper relies on this without support.
The SSO is flexible, gradient-free and less prone to the local optima trap.
“The SSO is flexible, gradient-free and less prone to the local optima trap.”
The paper relies on this without support.
Levy flight distribution is more effective than the uniform random distribution in the quest for space exploration.
“This distribution is more effective than the uniform random distribution in the quest for space exploration.”
The paper relies on this without support.
Metaheuristic algorithms achieve higher accuracy compared to existing algorithms.
“The metaheuristic algorithms achieve higher accuracy compared to existing algorithms.”
The paper relies on this without support.
The nature inspired salp swarm optimization (SSO) algorithm has showed its efficiency in various fields of engineering since its inception.
“The nature inspired salp swarm optimization (SSO) algorithm has showed its efficiency in various fields of engineering since its inception.”
The paper relies on this without support.
With early diagnosis and treatment, the risk of heart disease can be minimized.
“With early diagnosis and treatment, the risk of heart disease can be minimized.”
The paper relies on this without support.
The search strategy of the crow algorithm poses major challenges when faced with extremely multimodal formulations.
“The search strategy of the crow algorithm poses major challenges when faced with extremely multimodal formulations.”
The paper relies on this without support.
The median studentized residual approach removes undesirable or noisy data and enhances the recognition process of heart disease.
“The median studentized residual approach is applied to remove undesirable or noisy data because it investigates the correlation between the data in the dataset.This process of noise removal enhances the recognition process of heart disease.”
The paper relies on this without support.
Feature selection improves the performance of prediction algorithms.
“This process helps improve the performance of the prediction algorithms.”
The paper relies on this without support.
The IoMT device's remote tracking and cloud storage significantly increases the efficacy, accessibility and cost efficiency of the healthcare system.
“The health parameters of patients can be tracked remotely, constantly, in real-time and then stored and transmitted to the data center, such as the cloud, which significantly increases the efficacy, accessibility and cost efficiency of the healthcare system.”
The paper relies on this without support.
ANFIS can predict most applications when the proper number of rules is used.
“Evidence shows that the ANFIS can predict most of the applications by applying the proper number of rules.”
The paper relies on this without support.
Levy flight can achieve local optimization and efficiency in searching for a food location by maximizing search space diversity.
“Levy flight has the capability to achieve local optimization and efficiency in the search for a food location by maximizing the search space diversity.”
The paper relies on this without support.
The Hungarian and Framingham datasets are representative of the target population of heart disease patients.
“Two datasets (Hungarian and Framingham) are used in the simulation [23].”
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Different studies employ different imputation techniques (median-based, mean-based, average-value, and matrix factorization) for missing value repair.
“Different studies employ different imputation techniques (median-based [17], mean-based [46], average-value [18], and matrix factorization [44]) for missing value repair.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [17]
Those two techniques are part of data analytics frameworks.
“They are part of data analytics frameworks.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [17]
Only two data repair techniques [17,46] in the SLR have not been evaluated.
“Only two data repair techniques [17,46] in our SLR have not been evaluated.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [17]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
HRFLM accuracy is reported differently in two sections
MinorThe literature survey gives Mohan et al.'s HRFLM prediction accuracy as 88.7, whereas the comparative analysis gives HRFLM's accuracy as 88.4.
“The authors reported a prediction accuracy of 88.7.”
“HRFLM (88.4)”
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- Authors
- Khan, M. A., & Algarni, F.
- Affiliations
- University of Bisha (SA)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 122259-122269
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3039358234
- Semantic Scholar
- cef50ab69a765abe3571fbe70377eedd5c4d78ae
- Cited by
- 283
- References
- 29
- Citations by year
- 2026: 18 · 2025: 46 · 2024: 62 · 2023: 71 · 2022: 58 · 2021: 24 · 2020: 4
- FWCI
- 34.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Bisha
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart disease prediction, Internet of Medical Things, salp swarm algorithm, adaptive neuro-fuzzy inference system, wearable devices, healthcare monitoring, cloud computing, early disease detection, parameter optimization
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