Classification of lung cancer subtypes by data mining technique

Dass, Mayukh; Rasheed, Mohammed Abdul; Ali, Mohammed Mahmood

doi:10.1109/ciec.2014.6959151

Cited by 24 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An overview of methods for diagnosis of breast cancer from histopathological images is presented in [10]. The works in [11][12][13] have covered colon and lung cancer detection problem. The problem of Gleason grading and normal/abnormal classification on prostate tissue images is considered in [14][15][16] using fractal dimension, fractal code and color histogram features.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Noroozi

Zakerolhosseini

2016

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

“…cos (13) (14) in which atan2 is the arctangent function with two arguments. To illustrate the general shape of ) (n c′ , it is plotted in figure 3.a (in blue) for a set of typical values of…”

Section: Notes On Auto-encodersmentioning

confidence: 99%

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Noroozi

Zakerolhosseini

2016

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

“…In general, the rules have the form: if condition1 and condition2 and condition3 then outcome. In this work J48 algorithm [9] was utilized, it is an WEKA implementation of C4.5 decision tree algorithm.…”

Section: Data Partitioningmentioning

confidence: 99%

MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices

Sowjanya

Singhal

Choudhary

2015

2015 IEEE International Advance Computing Conference (IACC)

View full text Add to dashboard Cite

Diabetes mellitus (DM) is reaching possibly epidemic proportions in India. The degree of disease and destruction due to diabetes and its potential complications are enormous, and originated a significant health care burden on both households and society. The concerning factor is that diabetes is now being proven to be linked with a number of complications and to be occurring at a comparatively younger age in the country. In India, the migration of people from rural to urban areas and corresponding modification in lifestyle are all moving the degree of diabetes. Deficiency of knowledge about diabetes causes untimely death among the population at large. Therefore, acquiring a proficiency that should spread awareness about diabetes may affect the people in India. In this work, a mobile/android application based solution to overcome the deficiency of awareness about diabetes has been shown. The application uses novel machine learning techniques to predict diabetes levels for the users. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes prediction using real world dataset collected from a reputed hospital in the Chhattisgarh state of India.

show abstract

“…Resultant feature subset has been tested on Dermatology and Breast Cancer medical datasets [2]. Data mining approach has been proposed by researchers for the classification of lung cancer subtypes [3]. Analysis of effectiveness of data mining techniques in healthcare system, various approaches, tools and its effect in healthcare has been presented by researchers [4].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

IJET

View full text Add to dashboard Cite

Classification of lung cancer subtypes by data mining technique

Cited by 24 publications

References 10 publications

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

Computer assisted diagnosis of basal cell carcinoma using Z-transform features

MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices

Untitled

Contact Info

Product

Resources

About