Optimal decision tree fuzzy rule-based classifier for heart disease prediction using improved cuckoo search algorithm

Narayan, Subhashini; Gobal, Jagadeesh

doi:10.1504/ijbidm.2019.102810

Cited by 4 publications

(3 citation statements)

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“…Were considered for evaluation. The decision tree outperforms well in forceasting the survival rate of cancer when compared with logistic regression and decision tree (C5.0) yields higher accuracy of 86.9% in predicting the breast cancer [6]. Birth defects was analyzed using decision tree algorithms.…”

Section: Related Studiesmentioning

confidence: 99%

Feature based analysis of endometriosis using machine learning

Visalaxi

SudalaiMuthu²,

Ramalingam³

2023

IJEECS

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<span lang="EN-US">Machine learning is a cutting-edge technology used for predicting and diagnosing various diseases. Various machine learning algorithm facilitates the prediction. The decision tree belongs to learning algorithm that performs both classification and prediction. The decision tree constructs the tree-like to evaluate the best features. The decision tree performs well in the prediction of various diseases. Endometriosis is a recurrence disease that creates an emotional impact in women. Endometriosis is a lump-like structure that appears at several locations in reproductive organs of women. The diagnosis of endometriosis was predicted through scanning procedures and laparoscopic procedures. The symptoms identified from laparoscopic surgery were used as the features for predicting the severity of endometriosis. The symptoms include mass-like structure, tissue size, variation in tissue colour, and blockages in fallopian tubes. The decision tree analyze the features of endometriosis by using two criteria such as entropy and Gini index. The entropy and Gini index construct the tree by identifying the size of tissue as major influencing attributes. The Gini index outperforms well with training accuracy of 84.08% and test accuracy of 84.85.</span>

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Section: Related Studiesmentioning

confidence: 99%

Feature based analysis of endometriosis using machine learning

Visalaxi

SudalaiMuthu²,

Ramalingam³

2023

IJEECS

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“…We introduce a short demonstrative example of how the ACO algorithm is used to solve the classical TSP. For further Detection of epilepsy based on ECG [9] Predicting radiation toxicity in prostate cancer treatment [10] Heart disease prediction [11] Lung nodule detection based on thoracic computed tomography images [12] Lung nodule detection based on thin section CT images [13] Predicting diabetes [14] Classification of premature ventricular complexes [15] Mammography classification [16] EEG signal classification [17] Classification of lung cancer stages from free text pathology reports [18] Analysis of dermatology databases [19] Medical decision support [20] Computer aided diagnosis [21] Classification of Arabic medical texts [22] Classification of diseases in discharge summaries Genetics [23] Analysis of gene-gene and gene-environment interactions in genetic association study Portfolio analysis [24,25] Bankruptcy prediction GIS [26] Landslide prediction [27] Mapping wetlands [28,29] Land cover classification HMI [30,31] Body poses and gesture recognition [32] Handwriting recognition Autonomous driving [33] Classification of lighting conditions for driving scenes ICT [34] User behavior prediction on website [35] Event driven messaging system [36] Sports video indexing [37,38] Network anomaly detection [39] Image processing [40] Sentiment analysis on microblog [41] Finding people with emotional distress in social media [42] Detecting travel modes [43] Classification of data streams Quality and reliability…”

Section: An Example Of Ant Colony Optimization For Tspmentioning

confidence: 99%

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

Hossain

Ema

Sohn

2022

Applied Computational Intelligence and Soft Computing

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The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

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“…After decades of changes and development, data mining has become an interdisciplinary discipline that integrates relevant knowledge from multiple disciplines such as statistics, databases, machine learning, pattern recognition, intelligence, and parallel computing [6]- [8]. Since the development of data mining, the data objects we have studied have evolved from the original regular data to the current messy and huge data [9]. Therefore, the scope of research is getting wider and wider, and the technical requirements are getting higher.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

et al. 2020

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Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious. Data mining technology has developed vigorously in this environment, and it has shown more and more vitality. Based on Spark programming model, this paper designs the parallel extension of fuzzy c-means. In order to enhance the performance of fuzzy c-means parallel expansion, the improvement strategy of k-means during the initialization phase is borrowed, and k-means// is extended to fuzzy c-means to obtain better clustering performance. Combined with Spark's programming model, this paper can obtain extended parallel fuzzy c-means algorithm. Several experiments on the data set of the algorithm proposed in this paper have shown good scalability and parallelism, effectively expanding fuzzy c-means clustering to distributed applications, greatly increasing the scale of the data processed by the algorithm. This improves the robustness of the algorithm and the adaptability of the algorithm to the shape and structure of the data, so that the parallel and scalable clustering algorithm can more effectively perform cluster analysis on big data. Three algorithms were simulated on MATLAB platform. We use simple data sets and complex two-dimensional data sets, and compare with the traditional fuzzy c-means algorithm and fuzzy c-means algorithm based on fuzzy entropy. Experiments show that the scalable parallel fuzzy c-means algorithm not only greatly improves the anti-noise performance, but also improves the convergence speed, and it can automatically determine the optimal number of clusters. INDEX TERMS Artificial intelligence, data mining, cluster analysis, scalable parallel fuzzy c-means, cloud computing.

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Optimal decision tree fuzzy rule-based classifier for heart disease prediction using improved cuckoo search algorithm

Cited by 4 publications

References 0 publications

Feature based analysis of endometriosis using machine learning

Feature based analysis of endometriosis using machine learning

Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

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