Journal of Entrepreneurship & Management

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Empirical Analysis of Different Machine Learning Algorithms for Predicting Lung Cancer


Affiliations
1 Assistant Professor, Dept. of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
2 Professor and Head, Dept. of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India
     

   Subscribe/Renew Journal


In the current scenario, cancer disease is substantial menace to human life globally. About 32 percent of people worldwide are affected by various types of cancer. But lung cancer depicts the highest ratio. Nowadays peoples are not having awareness about to detect the cancer in early stage. The survival rate of five year for lung cancer disease is 55 percent of the cases are affected most. However, only 14 percent of lung tumor cases are diagnosed at an early stage. For slight tumors the five-year survival rate is simply 3 percent. There are 4 stages in lung cancer. If we predict the disease in I and II stage, it is easy to cure by effortless operations. If it exceeds second stage, it may not be cured. So, diagnosing the cancer in earlier stage is the best solution to predict the patients from death. For that, the system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as preferred classification model. By using these algorithms, it becomes easier to diagnose the cancer in early stage. So, the survival rate of lung cancer patients becomes higher. This comparative analysis, calculates and compares the precision of Random Forest, Naive Bayes and KNN and the preliminary result reveals that ID3 furnish better precision for cancer dataset. The input has been accessed only in numeric format. The algorithms also maintain key stuffs of the dataset, which are predominant for extracting performance, and so it may warrant the correct defense and effective preservation. This leads to protection of any extracting works that depends on the sequence of distances between objects, such as Random Forest, Naive Bayes -search and classification, as well as many visualization techniques. In particular, it establishes a restricted isometric property, in which it is the tight leap on the shrinkage and enlargement of the original distances.

Keywords

Machine Learning; Unsupervised Learning; Naive Bayes classifiers; Decision Tree; Random forest; Decision Support system; Neural network.
Subscription Login to verify subscription
User
Notifications
Font Size


  • . Andrzej Skalski; Jacek Jakubowski; Tomasz Drewniak, “Lung tumor segmentation and detection on Computed Tomography data”, Imaging Systems and Techniques (IST), 2016 IEEE International Conference on 4-6 Oct. 2016

  • . Aneesh kumar and. A. C. Jothi Venkateswaran, “Estimating the Surveillance of Lung Disorder using Classification Algorithms”, International Journal of Computer Applications (0975 – 8887), Volume 57– No.6, November 2012.

  • . Bard HSSINA, Abdel Karim MERBOUHA,” A comparative study of decision tree ID3 and C4.5”, International Journal of Computer Applications Beni-Mellal, BP: 523

  • . Bendi Venkata Ramana, Prof. M.Surendra Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Study of Selected Classification Algorithms for Lung Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3, No.2 (2011), PP.101-11.

  • . Cybenko.G, “Approximation by super positions of a sigmoidal function”, Mathematics of Control, Signals, and Systems, Vol.2 (1989), PP. 303-314.

  • . Emrana Kabir Hashi, Md. Shahid Uz Zaman and Md. Rokibul Hasan, “An Expert Clinical Decision Support System to Predict Disease Using Classification Techniques”, International Conference on Electrical, Computer and Communication Engineering (ECCE), February 16-18, 2017, Cox’s Bazar, Bangladesh (2017).

  • . Han Sang Lee; Helen Hong; Junmo Kim, “Detection and segmentation of small renal masses in contrast-enhanced CT images using texture and context feature classification”, Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on 18-21 April 2017, ISSN: 1945- 8452.

  • . Isabelle Guyon and Andr´eElisseeff,” An Introduction to Variable and Feature Selection”, Journal of Machine Learning Research 3 (2013) 1157-1182.

  • . Jankishran Pahariyavohra, Jagdeesh makhijani and sanjay patsariya, “Lung patient classification using intelligence techniques”, International journal of advanced research in computer science and software engineering, Volume 4, Issue 2, Pages 295-299,2013.

  • . John C. Platt,” Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Technical Report, April 21, 2010.

  • .Rajeswari P and Sophia Reena.G, “Analysis of Lung Disorder Using Data Mining Algorithm”, Global Journal of Computer Science and Technology, Vol. 10 Issue 14 (Ver. 1.0) November 2010.


Abstract Views: 280

PDF Views: 0




  • An Empirical Analysis of Different Machine Learning Algorithms for Predicting Lung Cancer

Abstract Views: 280  |  PDF Views: 0

Authors

M. Sharmila
Assistant Professor, Dept. of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
R. Punithavathi
Professor and Head, Dept. of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India

Abstract


In the current scenario, cancer disease is substantial menace to human life globally. About 32 percent of people worldwide are affected by various types of cancer. But lung cancer depicts the highest ratio. Nowadays peoples are not having awareness about to detect the cancer in early stage. The survival rate of five year for lung cancer disease is 55 percent of the cases are affected most. However, only 14 percent of lung tumor cases are diagnosed at an early stage. For slight tumors the five-year survival rate is simply 3 percent. There are 4 stages in lung cancer. If we predict the disease in I and II stage, it is easy to cure by effortless operations. If it exceeds second stage, it may not be cured. So, diagnosing the cancer in earlier stage is the best solution to predict the patients from death. For that, the system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as preferred classification model. By using these algorithms, it becomes easier to diagnose the cancer in early stage. So, the survival rate of lung cancer patients becomes higher. This comparative analysis, calculates and compares the precision of Random Forest, Naive Bayes and KNN and the preliminary result reveals that ID3 furnish better precision for cancer dataset. The input has been accessed only in numeric format. The algorithms also maintain key stuffs of the dataset, which are predominant for extracting performance, and so it may warrant the correct defense and effective preservation. This leads to protection of any extracting works that depends on the sequence of distances between objects, such as Random Forest, Naive Bayes -search and classification, as well as many visualization techniques. In particular, it establishes a restricted isometric property, in which it is the tight leap on the shrinkage and enlargement of the original distances.

Keywords


Machine Learning; Unsupervised Learning; Naive Bayes classifiers; Decision Tree; Random forest; Decision Support system; Neural network.

References