International Journal of Hematology-Oncology and Stem Cell Research 2009. 3(4):25-33.

A New Method for Diagnosis and Predicting Blood Disorder and Cancer Using Artificial Intelligence (Artificial Neural Networks)
Mehrdad Payandeh, Mehrnoush Aeinfar, Vahid Aeinfar, Mohsen Hayati


Abstract: This paper represents a novel use of artificial neural networks in medical science. The proposed technique involves training a Multi Layer Perceptron (MLP) (a kind of artificial neural network) with a BP learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from H1 machine. The blood test parameters and diagnosis of physician about the diseases of 450 patients from Taleghani Hospital in Kermanshah, Iran, are used in a supervised training method to update network parameters. This method was implemented to diagnose these disorder and cancer: Megaloblastic Anaemia, Thalassemia, Idiopathic thrombocytopenic pupura (ITP), Chronic myelogenous leukemia and Lymphoproliferative.


Artificial Neural Network; Multilayer Perceptron (MLP); Multi-Variable Non-linear Regression; Blood

Full Text:



Maiellaro PA, Cozzolongo R, Marino P. Artificial Neural Networks for the Prediction of Response to Interferon Plus Ribavirin Treatment in Patients with Chronic Hepatitis C. Current Pharmaceutical Design, 2004; 10: 2101-2109.

Karabatak M, Cevdet Ince M. An Expert System for Detection of Breast Cancer Based on Association Rules and Neural Network. Expert Systems with Applications, 2009; 36 (2), Part 2: 3465-3469.

Elif Derya Übeyli. Implementing Automated Diagnostic Systems for Breast Cancer Detection. Expert Systems with Applications, 2007; 33 (4): 1054-1062.

Tan TZ, Quek C, Ng GS, et al. A Novel Cognitive Interpretation of Breast Cancer Thermography with Complementary Learning Fuzzy Neural Memory Structure. Expert Systems with Applications, 2007; 33 (3): 652-666.

Lisboa PJ, Etchells TA, Jarman IH, et al. Time-to-event Analysis with Artificial Neural Networks: An Integrated Analytical and Rule-based Study for Breast Cancer. Neural Netw. 2008; 21(2-3): 414-26. Epub 2007 Dec 28.

Stephan C, Cammann H, Meyer HA, et al. An Artificial Neural Network for Five Different Assay Systems of Prostate- Specific Antigen in Prostate Cancer Diagnostics. BJU Int, 2008; 102: 799–805.

Stephan C, Xu C, Finne P, et al. Comparison of Two Different Artificial Neural Networks for Prostate Biopsy Indication in Two Different Patient Populations, Urology, 2007; 70 (3): 596-601.

Kattan WM. Editorial Comment on: Development, Validation, and Head-to-Head Comparison of Logistic Regression-Based Nomograms and Artificial Neural Network Models Predicting Prostate Cancer on Initial Extended Biopsy. Eur Urol, 2008; 54(3): 611. Epub 2008 Jan 15.

Kawakami S, Numao N, Okubo Y, et al. Development, Validation, and Head-to-Head Comparison of Logistic Regression-Based Nomograms and Artificial Neural Network Models Predicting Prostate Cancer on Initial Extended Biopsy. Eur Urol, 2008; 54(3): 601-11. Epub 2008 Jan 15.

Chun FKH, Graefen M, Briganti A, et al. Initial Biopsy Outcome Prediction Head-to-Head Comparison of a Logistic Regression-Based Nomogram versus Artificial Neural Network. Eur Urol, 2007; 51: 1236- 43.

Kurt I, Ture M, Kurum AT. Comparing Performances of Logistic Regression, Classification and Regression Tree, and Neural Networks for Predicting Coronary Artery Disease. Expert Systems with Applications, 2008; 34 (1): 366- 374.

Ari S, Saha G. In Search of an Optimization Technique for Artificial Neural Network to Classify Abnormal Heart

Mucke L. Strategies to prevent neural network dysfunction in Alzheimer. Alzheimer's and Dementia, 2008; 4 (4) Supp1: T102-T103.

Di Luca1 M, Grossi E, Borroni B, et al. Artificial Neural Networks Allow the Use of Simultaneous Measurements of Alzheimer Disease Markers for Early Detection of the Disease. J Transl Med, 2005; 3: 30. Published Online 2005 July 27. doi: 10.1186/1479-5876-3-30.

Uğuz H, Öztürk A, Saraçoğlu R, et al. A Biomedical System Based on Fuzzy Discrete Hidden Markov model for the diagnosis of the brain diseases, Expert Systems with Applications: An International Journal, 2008; 35 (3): 1104-1114

Qiua X, Taob N, Tana Y, et al. Constructing of the Risk Classification Model of Cervical Cancer by Artificial Neural Network. Expert Systems with Applications: An International Journal archive, 2007; 32 (4): 1094-1099.

Chang CL, Chena CH. Applying Decision Tree and Neural Network to Increase Quality of Dermatologic Diagnosis. Expert Systems with Applications, 2009; 36 (2) Part 2: 4035-4041.

Mataria M, Janech GM, Almeida J, et al. Prediction of Progression of Diabetic Nephropathy in a Small Set of Patients by Artificial Neural Networks and Proteomic Analysis. American Journal of Kidney Diseases, 2008; 51 (4): B67-B67.

Polata K, Karab S, Güvenc A, et al. Utilization of Discretization Method on the Diagnosis of Optic Nerve Disease. Computer Methods and Programs in Biomedicine, 2008; 91 (3): 255-264.

Tan ZT, Quek C, Ng SG, et al. Ovarian Cancer Diagnosis with Complementary Learning Fuzzy Neural Network. Artificial Intelligence in Medicine, 2008; 43 (3): 207-222.

Săftoiu A, Vilmann P, Gorunescu F, et al. Neural Network Analysis of Dynamic Sequences of EUS Elastography Used for the Differential Diagnosis of Chronic Pancreatitis and Pancreatic Cancer. Gastrointestinal Endoscopy, 2008; 67 (5): AB97.

Ubeyli ED, Guler I. Multilaver perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides. Neurocomputing, 2004; 62: 349-365.

Tu JV. Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Prediction Medical Outcomes. J Clin Epidemiol, 1996; 49: 1225-1231.

Liew LP, Lee CY, Lin CY, et al. Comparison of Artificial Neural Networks with Logistic Regression in Prediction of Gallbladder Disease Among Obese Patients. Digestive and Liver Disease, 2007; 39 (4): 356-362.


  • There are currently no refbacks.

Creative Commons Attribution-NonCommercial 3.0

This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.