A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

Ripan, Rony Chowdhury; Sarker, Iqbal H.; Hossain, Syed Md. Minhaz; Anwar, Musfique; Nowrozy, Raza; Hoque, Mohammed Moshiul; Furhad, Md. Hasan

doi:10.1007/s42979-021-00518-7

Cited by 52 publications

(23 citation statements)

References 30 publications

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“…Various events' conditional and marginal probabilities are compared by the Naïve Bayes algorithm. For the given samples, this algorithm is useful for calculating the possible nearest value [17]. e Bayes theorem is useful for calculating the diagnostic probability when the patient's health is monitored based on a few symptoms.…”

Section: Naïve Bayes Weighted Approach (Nbwa)mentioning

confidence: 99%

Machine Learning Technology-Based Heart Disease Detection Models

Nagavelli

Samanta

Chakraborty

2022

Journal of Healthcare Engineering

104

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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.

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Section: Naïve Bayes Weighted Approach (Nbwa)mentioning

confidence: 99%

Machine Learning Technology-Based Heart Disease Detection Models

Nagavelli

Samanta

Chakraborty

2022

Journal of Healthcare Engineering

104

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“…Angelin 19 represented a k ‐means clustering‐based outlier detection approach in a medical healthcare dataset, in which they modified k ‐means clustering using the dragon fly optimization algorithm. Ripan et al 8 proposed a k ‐means clustering‐based anomaly detection model in a heart disease dataset. In this study, they set max threshold and min threshold scores to detect anomalies within clusters.…”

Section: Related Workmentioning

confidence: 99%

“…For this purpose, we implemented the whole outlier detection process with DBSCAN and k ‐means again. For k ‐means we followed our previous study approach 8 . After that, we removed those outliers and compared precision, recall, and accuracy as before.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…However, effective modeling of cyber‐attacks is problematic as today's network intrusion datasets include high dimensions of security features and may include outliers. Outliers differ significantly from other observations and do not agree with normal behavior 8,9 . As a result, a cyber‐attacks prediction model with outliers may cause a variety of problems, including high variance, which typically leads to over‐fitting in a classification model, high computational cost and time for model training, a lack of model generalization, and, ultimately, decreased predictive accuracy in unseen test cases.…”

Section: Introductionmentioning

confidence: 99%

“…Outliers differ significantly from other observations and do not agree with normal behavior. 8,9 As a result, a cyber-attacks prediction model with outliers may cause a variety of problems, including high variance, which typically leads to over-fitting in a classification model, high computational cost and time for model training, a lack of model generalization, and, ultimately, decreased predictive accuracy in unseen test cases. Thus in this article, we propose an effective cyber-attacks prediction model through outlier detection, which is an extended version of our earlier paper.…”

Section: Introductionmentioning

confidence: 99%

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Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection

Ripan

Islam

Alqahtani

et al. 2022

Security and Privacy

Self Cite

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Due to the popularity of Internet of Things devices, the exponential progress of computer networks, and a plethora of associated applications, cybersecurity has recently attracted much attention in light of today's security problems. As a result, detecting various cyber‐attacks within a network and developing an effective cyber‐attacks prediction model that plays a crucial part in today's defense has become increasingly critical. Modeling cyber‐attacks effectively, on the other hand, is challenging because modern security datasets hold a large number of dimensions of security features and may contain outliers. To accomplish this, we provide an approach for categorizing cyber‐attacks effectively through isolation forest learning‐based outlier detection. Additionally, we apply a variety of popular machine learning approaches to assess the performance of cyber‐attacks prediction models, including logistic regression, support vector machine, AdaBoost classifier, naive Bayes, and K‐nearest neighbor. We evaluated the efficacy of our approach by running tests on three network intrusion datasets (KDD Cup 99, CIC‐IDS2017, and UNSW‐NB15) and computing the precision, recall, and accuracy. Experiments demonstrate that eliminating outliers improves the prediction accuracy of cyber‐attacks for different classifiers. Additionally, we compare the isolation forest learning‐based outlier detection model to other well‐known outlier detection techniques, DBSCAN and k‐means, and measure the effectiveness of our model.

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The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

Tripathi

2024

Health Care Science

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IntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

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A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

Cited by 52 publications

References 30 publications

Machine Learning Technology-Based Heart Disease Detection Models

Machine Learning Technology-Based Heart Disease Detection Models

Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection

The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection

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