In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of research. In recent years, significant attempts are made for the enhancement of computer aided diagnosis applications because errors in medical diagnostic systems can result in seriously misleading medical treatments. Machine learning is important in Computer Aided Diagnosis. After using an easy equation, objects such as organs may not be indicated accurately. So, pattern recognition fundamentally involves learning from examples. In the field of bio-medical, pattern recognition and machine learning promise the improved accuracy of perception and diagnosis of disease. They also promote the objectivity of decision-making process. For the analysis of high-dimensional and multimodal bio-medical data, machine learning offers a worthy approach for making classy and automatic algorithms. This survey paper provides the comparative analysis of different machine learning algorithms for diagnosis of different diseases such as heart disease, diabetes disease, liver disease, dengue disease and hepatitis disease. It brings attention towards the suite of machine learning algorithms and tools that are used for the analysis of diseases and decision-making process accordingly.
References
[1]
Marshland, S. (2009) Machine Learning an Algorithmic Perspective. CRC Press, New Zealand, 6-7.
[2]
Sharma, P. and Kaur, M. (2013) Classification in Pattern Recognition: A Review. International Journal of Advanced Research in Computer Science and Software Engineering, 3, 298.
[3]
Rambhajani, M., Deepanker, W. and Pathak, N. (2015) A Survey on Implementation of Machine Learning Techniques for Dermatology Diseases Classification. International Journal of Advances in Engineering & Technology, 8, 194-195.
[4]
Kononenko, I. (2001) Machine Learning for Medical Diagnosis: History, State of the Art and Perspective. Journal of Artificial Intelligence in Medicine, 1, 89-109.
[5]
Otoom, A.F., Abdallah, E.E., Kilani, Y., Kefaye, A. and Ashour, M. (2015) Effective Diagnosis and Monitoring of Heart Disease. International Journal of Software Engineering and Its Applications. 9, 143-156.
[6]
Vembandasamy, K., Sasipriya, R. and Deepa, E. (2015) Heart Diseases Detection Using Naive Bayes Algorithm. IJISET-International Journal of Innovative Science, Engineering & Technology, 2, 441-444.
[7]
Chaurasia, V. and Pal, S. (2013) Data Mining Approach to Detect Heart Disease. International Journal of Advanced Computer Science and Information Technology (IJACSIT), 2, 56-66.
[8]
Parthiban, G. and Srivatsa, S.K. (2012) Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients. International Journal of Applied Information Systems (IJAIS), 3, 25-30.
[9]
Tan, K.C., Teoh, E.J., Yu, Q. and Goh, K.C. (2009) A Hybrid Evolutionary Algorithm for Attribute Selection in Data Mining. Journal of Expert System with Applications, 36, 8616-8630. https://doi.org/10.1016/j.eswa.2008.10.013
[10]
Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J. and Rajabi, M.J. (2014) Advantage and Drawback of Support Vector Machine Functionality. 2014 IEEE International Conference on Computer, Communication and Control Technology (I4CT), Langkawi, 2-4 September 2014, 64-65.
https://doi.org/10.1109/i4ct.2014.6914146
[11]
Iyer, A., Jeyalatha, S. and Sumbaly, R. (2015) Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5, 1-14. https://doi.org/10.5121/ijdkp.2015.5101
[12]
Sen, S.K. and Dash, S. (2014) Application of Meta Learning Algorithms for the Prediction of Diabetes Disease. International Journal of Advance Research in Computer Science and Management Studies, 2, 396-401.
[13]
Kumari, V.A. and Chitra, R. (2013) Classification of Diabetes Disease Using Support Vector Machine. International Journal of Engineering Research and Applications (IJERA), 3, 1797-1801.
[14]
Sarwar, A. and Sharma, V. (2012) Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2. Special Issue of International Journal of Computer Applications (0975-8887) on Issues and Challenges in Networking, Intelligence and Computing Technologies-ICNICT 2012, 3, 14-16.
[15]
Ephzibah, E.P. (2011) Cost Effective Approach on Feature Selection using Genetic Algorithms and Fuzzy Logic for Diabetes Diagnosis. International Journal on Soft Computing (IJSC), 2, 1-10. https://doi.org/10.5121/ijsc.2011.2101
[16]
Archana, S. and DR Elangovan, K. (2014) Survey of Classification Techniques in Data Mining. International Journal of Computer Science and Mobile Applications, 2, 65-71
[17]
Vijayarani, S. and Dhayanand, S. (2015) Liver Disease Prediction using SVM and Naive Bayes Algorithms. International Journal of Science, Engineering and Technology Research (IJSETR), 4, 816-820.
[18]
Gulia, A., Vohra, R. and Rani, P. (2014) Liver Patient Classification Using Intelligent Techniques. (IJCSIT) International Journal of Computer Science and Information Technologies, 5, 5110-5115.
[19]
Rajeswari, P. and Reena,G.S. (2010) Analysis of Liver Disorder Using Data Mining Algorithm. Global Journal of Computer Science and Technology, 10, 48-52.
[20]
Tarmizi, N.D.A., Jamaluddin, F., Abu Bakar, A., Othman, Z.A., Zainudin, S. and Hamdan, A.R. (2013) Malaysia Dengue Outbreak Detection Using Data Mining Models. Journal of Next Generation Information Technology (JNIT), 4, 96-107.
[21]
Fathima, A.S. and Manimeglai, D. (2012) Predictive Analysis for the Arbovirus-Dengue using SVM Classification. International Journal of Engineering and Technology, 2, 521-527.
[22]
Ibrahim, F., Taib, M.N., Abas, W.A.B.W., Guan, C.C. and Sulaiman, S. (2005) A Novel Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) Analysis Using Artificial Neural Network (ANN). Computer Methods and Programs in Biomedicine, 79, 273-281. https://doi.org/10.1016/j.cmpb.2005.04.002
[23]
Ba-Alwi, F.M. and Hintaya, H.M. (2013) Comparative Study for Analysis the Prognostic in Hepatitis Data: Data Mining Approach. International Journal of Scientific & Engineering Research, 4, 680-685.
[24]
Karlik, B. (2011) Hepatitis Disease Diagnosis Using Back Propagation and the Naive Bayes Classifiers. Journal of Science and Technology, 1, 49-62.
[25]
Sathyadevi, G. (2011) Application of CART Algorithm in Hepatitis Disease Diagnosis. IEEE International Conference on Recent Trends in Information Technology (ICRTIT), MIT, Anna University, Chennai, 3-5 June 2011, 1283-1287.
[26]
Singh, Y., Bhatia, P.K., and Sangwan, O. (2007) A Review of Studies on Machine Learning Techniques. International Journal of Computer Science and Security, 1, 70-84.
