Performance Analysis of Data Mining Classification Techniques to Predict Diabetes

Perveen, Sajida; Shahbaz, Muhammad; Guergachi, Aziz; Keshavjee, Karim

doi:10.1016/j.procs.2016.04.016

Cited by 233 publications

(134 citation statements)

References 5 publications

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“…Sajida et al in [6] deliberated the role of Adaboost and Bagging ensemble machine learning methods [10] using J48 decision tree as the basis for classifying the Diabetes Mellitus and patients as diabetic or non-diabetic, based on diabetes risk factors. Results achieved after the experiment proves that, Adaboost machine learning ensemble technique outperforms well comparatively bagging as well as a J48 decision tree.…”

Section: Related Workmentioning

confidence: 99%

Minimal Rule-Based Classifiers using PCA on Pima-Indians-Diabetes-Dataset

Reddy*¹,

Nagendra²

2019

IJITEE

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Diabetes mellitus has been a very complex and chronic lifelong disease. Hence, it has been identified as a clinically highly significant disease which further attracted the healthcare industry to define most relevant clinical directories and also to apply efficient automated prediagnoses and further care. Rule-based classifier technique has proven its strength in diabetes diagnosis when used the computational methods. There has been a considerable development in the classifier's performance, to classify the disease, in the past decades by which they were highly recommended. In this paper, a classifier is proposed based on minimum rules which further uses Principal Component Analysis (PCA). To apply these techniques Pima Indians diabetes dataset is used from the UCI Machine LearningRepository. Set of experiments were conducted on the data set with PCA to evaluate the performance among the decision tree, Naïve Bayes, and Support Vector Machine.

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Section: Related Workmentioning

confidence: 99%

Minimal Rule-Based Classifiers using PCA on Pima-Indians-Diabetes-Dataset

Reddy*¹,

Nagendra²

2019

IJITEE

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“…The cost effectiveness and human effects has been reduced using proposed prediction system based data mining. Mustafa A. Al-Fayoumi [11]proposed an Associative Classification based on Incremental Mining (ACIM) algorithm in order to maintain the huge amount of information. Sajida Perveen, et.al [12] presented that J48 decision tree was utilized in order to apply adaboost and bagging ensemble methods in order to differentiate patients that are suffering from diabetes mellitus based on different elements that can cause diabetes.…”

Section: Litrature Surveymentioning

confidence: 99%

Heart Disease Prediction Using Data Mining Techniques: A Survey

Kaur¹,

Arora²

2018

ijarcs

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Heart disease is a common cause of death for people around the world. The overall examination on reasons for death because of coronary illness has been watched that it is the real reason for death. Analysis of these issues at beginning period helps the doctors in treating it at starting stage and to enhance the patient's wellbeing. In this manner the need to treat coronary illness that is found in individuals which precise entangled issues, if overlooked at beginning time. Different Data Mining Techniques can be used to analyze heart related issues. The essential point is an analysis of the Data Mining technique which is generally exact. There are different types of Data Mining Techniques such as Decision Tree, Naïve Bayesian, Support Vector Machine (SVM), K-NN classifier,Hybrid Approach, ArtificialNeural Network ANN). In this paper, we analyze different classification algorithms.

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“…Another strategy for diabetic patient classification was proposed by combining J48 decision tree, Bagging, and AdaBoost using J48 as a base learner. They performed the experimentation using Weka tool [21]. Some other researchers worked on a cloud-based health care system that aggregates the data from the Wireless Body Area Networks (WBAN) and later then the real-time analytics were done by combining the concept of STORM and the fuzzy logic [22].…”

Section: Web-based Systems For Healthcare Monitoringmentioning

confidence: 99%

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

et al. 2018

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Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.

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Performance Analysis of Data Mining Classification Techniques to Predict Diabetes

Cited by 233 publications

References 5 publications

Minimal Rule-Based Classifiers using PCA on Pima-Indians-Diabetes-Dataset

Minimal Rule-Based Classifiers using PCA on Pima-Indians-Diabetes-Dataset

Heart Disease Prediction Using Data Mining Techniques: A Survey

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

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