Utilization of Data Mining Techniques in Knowledge Extraction for Diminution of Diabetes

Nuwangi, S.M.; Oruthotaarachchi, Chalani; Tilakaratna, J.M.P.P.; Caldera, H. A.

doi:10.1109/vcon.2010.8

Cited by 8 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Algoritma klasifikasi data mining tersebut dapat dimanfaatkan dan membantu ahli medis dalam mendiagnosa suatu penyakit, salah satunya adalah diabetes. Penyakit diabetes merupakan salah satu penyakit yang mematikan, faktor resiko tinggi dalam keluarga yang menyebabkan penyakit diabetes antara lain dikarenakan orang gemuk yang tidak melakukan latihan fisik, dan orang-orang yang memiliki gaya hidup yang tidak sehat dan makanan yang berlebih dari apa yang dibutuhkan oleh tubuh [4].…”

Section: Pendahuluanunclassified

Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes

Mirqotussa’adah¹,

Muslim²,

Sugiharti³

et al. 2017

Lontar Komputer

View full text Add to dashboard Cite

Pada bidang kesehatan, data mining dapat dimanfaatkan untuk memprediksi suatu penyakit dari data rekam medis pasien, diantaranya diabetes. Ada beberapa model data mining salah satunya klasifikasi. Di bidang klasifikasi, ada banyak cabang yang berkembang yaitu pohon keputusan (decision tree). Salah satu decision tree yang populer adalah C4.5. Dalam riset ini, data yang digunakan adalah pima indian diabetes dataset yang diambil dari UCI repository of machine learning. Pada dataset ini seluruh atributnya bertipe numerik yang bersifat continuous dan untuk menangani data continuous digunakan discretization. Akurasi sangat penting dalam pengklasifikasian, ensemble method adalah metode yang digunakan untuk meningkatkan akurasi algoritma klasifikasi dengan membangun beberapa classifier dari data training. Dari hasil penelitian, dengan menerapkan discretization dan teknik bagging untuk klasifikasi berbasis ensemble pada algoritma C4.5 dapat meningkatkan akurasi sebesar 6,26%. Dengan akurasi awal 68,61%, setelah diterapkan discretization dan teknik bagging menjadi 74,87%.

show abstract

Section: Pendahuluanunclassified

Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes

Mirqotussa’adah¹,

Muslim²,

Sugiharti³

et al. 2017

Lontar Komputer

View full text Add to dashboard Cite

show abstract

“…By using various classifiers, the authors propose a hybrid model for the prediction of the positivity and negativity of the diabetes. The research [11] uses association rule generation and classification techniques to support decision making by considering a data set of diabetes type 1 and type 2 patients. Identified gender female is a major decision factor of high fasting blood sugar level.…”

Section: Clustering and Classificationmentioning

confidence: 99%

“…Through the data mining techniques, a direct and strong relationship between edema and diabetes as well as wheezing and diabetes was identified. According to the doctor's view points, the probability of developing diabetes for male and female are the same [11]. But various research studies state that females are having a higher risk for having diabets.…”

Section: Clustering and Classificationmentioning

confidence: 99%

A Survey on Data-Mining Technologies for Prediction and Diagnosis of Diabetes

Shivakumar¹,

Alby

2014

2014 International Conference on Intelligent Computing Applications

View full text Add to dashboard Cite

The recent report of WHO shows a remarkable hike in the number of diabetic patients and this will be in the same pattern in the coming decades also. Early identification of diabetes is an important challenge. Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetesrelated data. Various data mining techniques help diabetes research and ultimately improve the quality of health care for diabetes patients. This paper provides a survey of data mining methods that have been commonly applied to Diabetes data analysis and prediction of the disease.

show abstract

“…The impact of preprocessing of diabetes data in artificial neural networking based technique is also examined [11].The research in the field of diabetes has also been studied based on Association Rules [12] [13]. This research takes advantage from the support system which proposed the process of knowledge extraction with the help of Data Mining [14] [15]. The idea of weighted classifier is formed the basis of this newly proposed approach based on applied weights of different attributes [16].…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach towards the Prediction of Likelihood of Diabetes

Khanna

Agarwal

2013

2013 International Conference on Machine Intelligence and Research Advancement

View full text Add to dashboard Cite

With the growth of Information and communication technologies, the health care industry is also producing extensively large data. For managing such large amount of data, an efficient knowledge discovery process is required. This field is developing fast and there is a big scope of early planning towards the treatment of large number of diseases. The planning can be done by developing some strategic solutions based on Data Mining for the treatment of the disease. Classification based on supervised learning is a technique of DataMining which helps in predicting the label of unknown samples as Class. This is extremely popular technique of Data Mining by which the treatment of a disease could be planned at an early stage. Diabetes is one of the chronic diseases produces metabolism disorder in human bodies. Metabolism refers a chemical process in human body responsible for energy conversion and utilization. The diabetes with type 1 and type 2 indicates excess glucose level in the blood could be cured if regular precautions have been taken persistently under certain clinical guidelines. This paper performs classification on diabetes dataset taken from SGPGI, Lucknow (A super specialty hospital in Lucknow, Uttar Pradesh, India).It predicts an unknown class label for given set of data and helpful to find out whether the class label for the dataset under consideration would be of low risk, medium risk or high risk. The classifier is further trained on the basis of weights assigned to different attributes which are generated by means of expert guidelines. The accuracy of classifier is verified by kappa statistics and accuracy, evolution criteria for classifiers

show abstract

Utilization of Data Mining Techniques in Knowledge Extraction for Diminution of Diabetes

Cited by 8 publications

References 5 publications

Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes

Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes

A Survey on Data-Mining Technologies for Prediction and Diagnosis of Diabetes

An Integrated Approach towards the Prediction of Likelihood of Diabetes

Contact Info

Product

Resources

About