Implicating Machine Learning towards Development of Intelligent System for Wart Treatment Therapy Identification
Keywords:Warts, Immunotherapy, Cryotherapy, Machine Learning.
Warts are produced on the human body because of contamination caused by Human Papillomavirus (HPV). The most affected areas of warts are hands and feet specifically, which is bit annoying and hard to recover in later stages. After massive literature survey, it is found that different treatments have been suggested for treating this illness. The basic problem found while treating that the treatment given to one patient may or may not be suitable for another patient, so it is hard to detect specific kind of treatment to be followed for faster recovery and effective customized treatment against this viral disease. The objective of this research work is to identify the ideal treatment method for both particular plantar and normal warts, between immunotherapy and cryotherapy treatment methods. Implications of machine learning techniques are now playing a vital role specifically in clinical diagnosis toward identifying different clinical patterns, disease classification and its predictions. In this research, work authors have implemented classifiers like Bayes Net, SVM, Multi-Layer Perceptron, k-NN, FURIA, Random Forest with the help of WEKA tool. The experimentation has been performed on data sets obtained from UCI Machine Learning Repositories. The experimentation was performed with total 180 patient instances having wart illness present in immunotherapy and cryotherapy datasets respectively. The result outcomes have been discussed and compared with existing methodologies mentioned in the literature. It was observed that the decision tree based classifier random forest is having the best classification accuracy among the chosen set of classifiers. The result shows highest classification accuracy in case random forest, 86% and 93% was noted for immunotherapy and cryotherapy treatment methods datasets. This research work is helpful for physicians in selecting the best treatment method for their patient suffering from wart illness in order to reduce overall treatment cost incurred and also improving the quality of treatment given to the patients.
 I.Kononenko, Machine learning for medical diagnosis: history,state of the art and perspective, Artif. Intell.Med23(2001) 89-109.
 K.J Cios, G.W. Moore, Uniqueness of medical data mining. Artif. Intell. Med. 26(2002)1-24.
 D.Delen, G.Waller, A.Kadam, Prediction breast cancer survivability a comparison of three data mining methods, Artif.Intell.Med 34(2006) 113-127.
 R.Bellazzi, B.Zupan, Predicting data mining in clinical medicine: current issues and guidelines, Int. J. Med.Inform. 77(2008), 81-97.
 Anooj P.K, Clinical decision support system:Risk level prediction of heart disease using weighted fuzzy rules.J.King Saud Univ.Comput.Inf.Sci.24(2012)27-40.
 S.Swati, G.Ashok,Feature selection for medical diagnosis: Evalution for cardiovascular disease. Expert Syst.Appl.40 (2013)4146-4153.
 N. Jesmin, K.Tasadduq lmam,TS. Kevin, PC.Yi-Ping, Computation intelligence for heart disease diagnosis: A medical knowledge driven approach. Expert Syst.Appl.40(2013)96-104.
 N. Jesmin, K.Tasadduq lmam,TS. Kevin, PC.Yi-Ping, Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst.Appl.40(2013)1086-1093.
 N.Esfandiary, M.Babavalian, A.Moghadam, V.Tabar, Knowledge discovery in medicine: Current issue and future trend.Expert Syst.Appl.41(2014)4434-4463.
 A.Majid, S.Ali, M.Iqbal, N.Kausar,Prediction of human breast and colon cancers from imbalanced data using neareat neighbor and support vector machines.Comput.Methods Prog. Biomed.113(2014) 792-808.
 C. Barbieri, F.Mari, A.Stopper, E.Gatti, P.Escandell-Montero, JM.Martinez-Mzrtinez, JD.Martin-Guerrero, A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis.Comput Biol Med.61(2015)56-61.
 L.Verma, S.Srivastava , P.C.Negi, A hybrid data mining model to predict coronary artery disease cases using non- invasive clinical data, J.Med.Syst.40(2016)1-7.
 T.Manikandan, N.Bharathi, Lung cancer detection using fuzzy auto-seed cluster means morphological segmentation and SVM classifier, J.Med.Syst.40(2016)
 F.Khozeimeh,R.Alizadehsani, M.Roshanzamir, A.Khosravi, P.Layegh, S.Nahavandi,An expert system for selecting wart treatment method.Comput Biol Med.81(2017)167-175.
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).