Survey of Machine Learning Algorithms for Disease Diagnostic

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.

[1]  Yogesh Singh,et al.  A REVIEW OF STUDIES ON MACHINE LEARNING TECHNIQUES , 2007 .

[2]  S. Jeyalatha,et al.  Diagnosis of diabetes using classification mining techniques , 2015, ArXiv.

[3]  Stephen Marsland,et al.  Machine Learning: An Algorithmic Perspective, Second Edition , 2014 .

[4]  Yousef Kilani,et al.  Effective Diagnosis and Monitoring of Heart Disease , 2015 .

[5]  Saurabh Pal,et al.  Data Mining Approach to Detect Heart Diseases , 2014 .

[6]  G.Sophia Reena,et al.  Analysis of Liver Disorder Using Data mining Algorithm , 2010 .

[7]  Dr. S. Vijayarani,et al.  Liver Disease Prediction using SVM and Naïve Bayes Algorithms , 2015 .

[8]  Priyanka Sharma,et al.  Classification in Pattern Recognition: A Review , 2013 .

[9]  Fadl Mutaher Ba-Alwi,et al.  Comparative Study for Analysis the Prognostic in Hepatitis Data : Data Mining Approach , 2013 .

[10]  Abid Sarwar,et al.  Intelligent Naïve Bayes Approach to Diagnose Diabetes Type-2 , 2012 .

[11]  Azuraliza Abu Bakar,et al.  Malaysia Dengue Outbreak Detection Using Data Mining Models , 2013 .

[12]  S. Archana,et al.  Survey of Classification Techniques in Data Mining , 2014 .

[13]  Bekir Karlik.  Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers , 2012 .

[14]  Kay Chen Tan,et al.  A hybrid evolutionary algorithm for attribute selection in data mining , 2009, Expert Syst. Appl..

[15]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[16]  Rajan Vohra,et al.  Liver Patient Classification Using Intelligent Techniques , 2014 .

[17]  E. P. Ephzibah,et al.  Cost effective approach on feature selection using genetic algorithms and fuzzy logic for diabetes diagnosis , 2011, ArXiv.

[18]  S. K. Srivatsa,et al.  Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients , 2012 .

[19]  Shahidan M. Abdullah,et al.  Advantage and drawback of support vector machine functionality , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[20]  Fatimah Ibrahim,et al.  A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN) , 2005, Comput. Methods Programs Biomed..

[21]  G. Sathyadevi,et al.  Application of CART algorithm in hepatitis disease diagnosis , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[22]  Somula Ramasubbareddy,et al.  Classification of Heart Disease Using Support Vector Machine , 2019, Journal of Computational and Theoretical Nanoscience.

[23]  A. Shameem Fathima,et al.  Predictive Analysis for the Arbovirus-Dengue using SVM Classification , 2012 .