Diagnosis of Malignant Pleural Mesothelioma Using KNN

Malignant pleural mesothelioma is a major health issue and is a cause of the concern as the number of cases are increasing constantly. The early diagnosis is necessary for the survival of the affected. An approach for the diagnosis of Mesothelioma has been proposed in this paper, which distinguishes normal person and the patient. The proposed approach uses a Differential Evolution Based Feature Selection Method, the input to which is a combination of the results from Mutual Information Maximization and Sequential Backward Selection. For classification K-Nearest Neighbor has been used and the average classification accuracy achieved is 99.07%.

[1]  Giovanni Petris,et al.  Small orangiophilic squamous‐like cells: An underrecognized and useful morphological feature for the diagnosis of malignant mesothelioma in pleural effusion cytology , 2014, Cancer cytopathology.

[2]  Nick A Maskell,et al.  Malignant pleural mesothelioma: an update on investigation, diagnosis and treatment , 2016, European Respiratory Review.

[3]  Bulusu Lakshmana Deekshatulu,et al.  Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm , 2015, ArXiv.

[4]  A. Vadivel,et al.  A New Feature Reduction Method for Mammogram Mass Classification , 2011 .

[5]  Adel Al-Jumaily,et al.  Feature subset selection using differential evolution and a statistical repair mechanism , 2011, Expert Syst. Appl..

[6]  Domenica Cavone,et al.  Epidemiological patterns of asbestos exposure and spatial clusters of incident cases of malignant mesothelioma from the Italian national registry , 2015, BMC Cancer.

[7]  R. Stahel,et al.  Malignant pleural mesothelioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2010, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  Fevzullah Temurtas,et al.  An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease , 2012, Comput. Electr. Eng..

[9]  V Torri,et al.  Malignant Pleural Mesothelioma : State of the art and recommendations , 2016 .

[10]  Orhan Er,et al.  Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma , 2015 .

[11]  Manpreet Kaur,et al.  An approach for feature selection using local searching and global optimization techniques , 2017, Neural Computing and Applications.

[12]  Mehrbakhsh Nilashi,et al.  An analytical method for diseases prediction using machine learning techniques , 2017, Comput. Chem. Eng..