Prediction of leukemia by classification and clustering techniques

Abstract Leukemia is a kind of blood cancer that impacts the white blood cells and damages the bone marrow. Typically the complete blood count (CBC) and bone marrow are affected. It can be a fatal disease if not identified at the earliest stage. Usually, manual microscopic assessment of stained sample slides is used for analysis of leukemia, but manual diagnostic strategies are time consuming, less accurate, and prone to errors due to diverse human elements such as pressure, fatigue, and so on. To avoid possible faults and errors and to assist pathologists, clustering and classification techniques are required, which are being used in every medical field to obtain better outcomes. This chapter emphasizes clustering and classification techniques applied to detection of leukemia. The proposed work consists of two phases: Phase I deals with the collection of the dataset and visualization of datasets, and Phase II deals with machine learning and data mining techniques for the prediction of leukemia. We would expect that the proposed techniques would show better performance than other existing techniques. The proposed techniques could be utilized for other diseases as well.

[1]  Monica L Guzman,et al.  Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data. , 2008, Blood.

[2]  Sanchita Paul,et al.  GA_SVM: A Classification System for Diagnosis of Diabetes , 2017 .

[3]  Matthias Becker,et al.  Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics , 2019, iScience.

[4]  Sanchita Paul,et al.  Implementation and Analysis of Classification Algorithms for Diabetes. , 2020, Current medical imaging.

[5]  Sanchita Paul,et al.  GA_RBF NN: a classification system for diabetes , 2017 .

[6]  Santosh Kumar,et al.  Classification of Diabetes using Deep Learning , 2020, 2020 International Conference on Communication and Signal Processing (ICCSP).

[7]  Sudhakar Tripathi,et al.  Classification of Diabetes by Kernel based SVM with PSO , 2019 .

[8]  Sudhansu Kumar Mishra,et al.  Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images. , 2020, Current medical imaging.

[9]  Abdollah Dehzangi,et al.  A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer , 2019, PloS one.

[10]  S. Prakasam,et al.  Effectiveness of Data Mining - based Cancer Prediction System (DMBCPS) , 2013 .

[12]  D. K. Choubey,et al.  Comparative Analysis of Classification Methods with PCA and LDA for Diabetes. , 2020, Current diabetes reviews.

[13]  D. Nigam,et al.  CANCER RESEARCH THROUGH THE HELP OF SOFT COMPUTING TECHNIQUES: A SURVEY , 2013 .

[14]  Sumita Mishra,et al.  Automated Detection of Acute Leukemia using K-mean Clustering Algorithm , 2016, ArXiv.

[15]  Samabia Tehsin,et al.  Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks , 2018, Technology in cancer research & treatment.

[16]  Mohamed Elhoseny,et al.  Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis , 2018 .

[17]  Sanchita Paul,et al.  Classification techniques for diagnosis of diabetes: a review , 2016 .

[19]  Christoph Schmid,et al.  Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  Dilip Kumar Choubey,et al.  Soft Computing, Data Mining, and Machine Learning Approaches in Detection of Heart Disease: A Review , 2019 .

[21]  R. M. Chandrasekar,et al.  Performance and Evaluation of Data Mining Techniques in Cancer Diagnosis , 2013 .

[22]  Ashraf Y. A. Maghari,et al.  Prediction and diagnosis of leukemia using classification algorithms , 2017, 2017 8th International Conference on Information Technology (ICIT).

[23]  Sanchita Paul,et al.  Classification Techniques for Thunderstorms and Lightning Prediction: A Survey , 2018 .

[24]  Yan Li,et al.  Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method , 2016, Comput. Math. Methods Medicine.

[25]  Prabhat Kumar,et al.  Performance evaluation of classification methods with PCA and PSO for diabetes , 2020 .

[26]  A. Morris,et al.  Pharmacogenomics of statin-related myopathy: Meta-analysis of rare variants from whole-exome sequencing , 2019, PloS one.

[27]  Zhong-Hui Duan,et al.  Gene Expression Based Leukemia Sub-Classification Using Committee Neural Networks , 2009, Bioinformatics and biology insights.

[28]  Debabrata Singh,et al.  EAC: Efficient Associative Classifier for Classification , 2019, 2019 International Conference on Applied Machine Learning (ICAML).

[29]  J. Byrd,et al.  Mass Cytometry: A High-Throughput Platform to Visualize the Heterogeneity of Acute Myeloid Leukemia. , 2015, Cancer discovery.

[30]  Manish Kumar,et al.  Implementation of a Hybrid Classification Method for Diabetes , 2019, Intelligent Innovations in Multimedia Data Engineering and Management.

[31]  Igor V. Tetko,et al.  Gene selection from microarray data for cancer classification - a machine learning approach , 2005, Comput. Biol. Chem..

[32]  Hugo Jair Escalante,et al.  Acute leukemia classification by ensemble particle swarm model selection , 2012, Artif. Intell. Medicine.

[33]  Dilip Kumar Choubey,et al.  Analysis of Liver Disorder Using Classification Techniques: A Survey , 2020, 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE).

[34]  Dilip Kumar Choubey,et al.  A Comparative study using Machine Learning and Data Mining Approach for Leukemia , 2020, 2020 International Conference on Communication and Signal Processing (ICCSP).

[35]  Sanchita Paul,et al.  GA_J48graft DT: A Hybrid Intelligent System for Diabetes Disease Diagnosis , 2015, BSBT 2015.

[36]  Sanchita Paul,et al.  Soft computing and data mining techniques for thunderstorms and lightning prediction: A survey , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[37]  Sanchita Paul,et al.  Rule based diagnosis system for diabetes , 2017 .

[38]  Julio J. Valdés,et al.  Gene Discovery in Leukemia Revisited: A Computational Intelligence Perspective , 2004, IEA/AIE.

[39]  Dr. S. P. Rajagopalan,et al.  An automatic Oral Cancer Classification using Data Mining Techniques , 2013 .

[40]  Arunkumar Sivaraman,et al.  Optimistic Diagnosis of Acute Leukemia Based OnHuman Blood Sample Using Feed Forward BackPropagation Neural Network , 2014 .

[41]  H. Sone,et al.  Patient‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach , 2019, Cancer medicine.

[42]  B. K. Tripathy,et al.  A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set , 2012 .