An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, &amp;amp;amp; CART Ensembles

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan; Javed, Muhammad Awais

doi:10.1109/fit.2014.50

Cited by 41 publications

(18 citation statements)

References 9 publications

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“…Ture et al [108] predicted risk factors for recurrence in determining recurrence-free survival of breast cancer patients. Bashir et al [109] classified, diagnosed, and predicted diabetes disease. Thenmozhi and Deepika [110] classified and predicted heart diseases based on a different attribute selection measures, such as information gain, gain ratio, gini index, and distance measure.…”

Section: Applicationsmentioning

confidence: 99%

“…Metabolic diseases [74,79] Clustering K-means Clustering [87] Clustering DBSCAN [171] Regression Random Forest [100] Classification SVM [106,109] Classification ID3 [115,116,118,120,122] Classification KNN [135] Classification Naïve Bayes [137,143] Classification Bayesian Networks [145] Regression Linear regression Cancer [75,81] Clustering K-means Clustering [84,86] Clustering DBSCAN [24] Clustering SNF [25] Clustering PINS [26] Clustering CIMLR [95,172] Classification SVM [108] Classification ID3 [130] Classification Naïve Bayes [136] Classification Bayesian Networks [148,173] Regression Linear regression [146,174] Regression Logistic regression [157] Classification Neural Networks + KNN [156] Classification Neural Networks + SVM [160] Classification Neural Networks + ID3 [161] Classification KNN [175] Classification DT [176] Classification DL…”

Section: Author Goal Algorithmmentioning

confidence: 99%

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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

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Section: Applicationsmentioning

confidence: 99%

Section: Author Goal Algorithmmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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“…Selecting the attribute recursively with the lowest Gini Index is the way of how the tree is constructed. Gini Index is calculated based on the formula below, where the probability of the ℎ class for target classes of a given attribute is , meanwhile, is the probability of class (Bashir et al, 2014).…”

Section: Learning Processmentioning

confidence: 99%

Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system

Arridha

Sukaridhoto

Pramadihanto

et al. 2017

IJSSC

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Abstract:Monitoring water conditions in real-time is a critical mission to preserve the water ecosystem in maritime and archipelagic countries, such as Indonesia that is relying on the wealth of water resources. To integrate the water monitoring system into the big data technology for realtime analysis, we have engaged in the ongoing project named SEMAR (Smart Environment Monitoring and Analytic in Real-time system), which provides the IoT-Big Data platform for water monitoring. However, SEMAR does not have an analytical system yet. This paper proposes the analytical system for water quality classification using Pollution Index method, which is an extension of SEMAR. Besides, the communication protocol is updated from REST to MQTT. Furthermore, the real-time user interface is implemented for visualisation. The evaluations confirmed that the data analytic function adopting the linear SVM and Decision Tree algorithms achieves more than 90% for the estimation accuracy with 0.019075 for the MSE.

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“…Using F-score feature selection was done and to obtain optimal set of features, k-mean clustering techniques were used. (Bashir.et.al, 2011) A hybrid model was recommended for improving diabetes diagnosis and classification precision.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[8]For each attribute Gini index is calculated and then attribute having lowest Gini index is chosen as the splitting attribute.…”

Section: Fig 1: Id3 -Decision Tree Decision Tree Based On Gini Index mentioning

confidence: 99%

A Compendium on the Contrasting Features of Data Mining Algorithms for the Diagnosis of Diabetes

Gandhi¹,

Deulkar²

2015

IJCA

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Data mining technology offers a user-oriented technique to hidden and novel patterns in the data. Because of rising diseases in the world, we are unable to evaluate all types of diseases and how to consume correct medicine for various diseases. Techniques of data mining are quite useful in finding the medicinal decision for the suitable diseases. Diabetes monitoring system is beneficial to diabetes patients. The diabetes system is useful not just for diabetes patients but also for those suspecting they are diabetic. The primary goal of this paper is to conduct a comparative analysis of decision tree algorithms namely ID3 and CART method focusing on diagnosis of diabetes.

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An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, &amp; CART Ensembles

Cited by 41 publications

References 9 publications

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system

A Compendium on the Contrasting Features of Data Mining Algorithms for the Diagnosis of Diabetes

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