INTERACTIVE NEURO-FUZZY EXPERT SYSTEM FOR DIAGNOSIS OF LEUKEMIA

Leukemia is closely linked with the blood or bone marrow. Leukemia is regard as cancer of the blood cells (usually white blood cells). The abnormal white blood cells formed in leukemia also accumulate in the organs of the body such as the spleen, liver, lymph nodes, testes, and brain, and interfere with normal functioning of the organ. Leukemia is of four common type; Acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic leukemia and chronic myelogenous leukemia. Leukemia symptoms are predominantly paleness, fatigue, bone pain, asthemia, palpitation, frequent infection, nose bleeding and thrombocytopenia. Neuro-Fuzzy Logic explores approximation techniques from neural networks to finds the parameter of a fuzzy system. In this paper, the traditional procedure for the medical diagnosis of leukemia employed by physician is analyzed using neuro-fuzzy inference procedure. From the system designed if the patient is having five or more of the enlisted symptoms, the patient is experiencing 201C;severe Leukemia201D; and should go for treatment urgently. If it is approximately four of the symptoms the patient is experiencing, the patient 201C;might be suffering from Leukemia201D; and hence should see a physician right away, but if it is three or less of the enlisted symptoms, the patient is not 201C;suffering from Leukemia201D;. The system which demonstrates the practical application of Information and Communication Technology (ICT) in the health sector is interactive and tells the patient his current condition as regards Leukemia

Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.Haneen Banjar, David Adelson, Fred Brown, and Naeem Chaudhr

Fuzzy Inspired Case based Reasoning for Hematology Malignancies Classification

Conventional approaches for collecting and reporting hematological data as well as diagnosing hematologic malignancies such as leukemia, anemia, e.t.c are based on subjective professional physician personal opinions or experiences which are influenced by human error, dependent on human-to-human judgments, time consuming processes and the blood results are non-reproducible. In the light of those human limitations identified, an automatic or semi-automatic classification and corrective method is required because it reduces the load on human observers and accuracy is not affected due to fatigue. Case-Based Reasoning (CBR) as a multi-disciplinary subject that focuses on the reuse of past experiences or cases to proffer solution to new cases was adopted and combined with the power of Fuzzy logic to design a software model that will effectively mine hematology data. This study aim at helping the medical practitioners to diagnose and provide corrective treatment to both normal patients and patients with hematology disorder at the early stage which can reduce the number of deaths. This aim is achievable by developing an intelligent expert system based on fuzzy logic and case-based reasoning for classification of hematology malignancy

Personalized medicine support system : resolving conflict in allocation to risk groups and predicting patient molecular response to targeted therapy

Treatment management in cancer patients is largely based on the use of a standardized set of predictive and prognostic factors. The former are used to evaluate specific clinical interventions, and they can be useful for selecting treatments because they directly predict the response to a treatment. The latter are used to evaluate a patient’s overall outcomes, and can be used to identify the risks or recurrence of a disease. Current intelligent systems can be a solution for transferring advancements in molecular biology into practice, especially for predicting the molecular response to molecular targeted therapy and the prognosis of risk groups in cancer medicine. This framework primarily focuses on the importance of integrating domain knowledge in predictive and prognostic models for personalized treatment. Our personalized medicine support system provides the needed support in complex decisions and can be incorporated into a treatment guide for selecting molecular targeted therapies.Haneen Banjar, David Adelson, Fred Brown, and Tamara Leclerc

Classification of blast cell type on acute myeloid leukemia (AML) based on image morphology of white blood cells

AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the WBC, the myeloblast cells, promyelocyte, and myelocyte. This makes the diagnosis process of leukemia subtype requires identification of blast cells in WBC. Automatic blast cell identification is widely developed but is constrained by the lack of data availability, and uneven distribution for each type of blast cell, resulting in problems of data imbalance. This makes the system developed has poor performance. This study aims to classify blast cell types in WBC identified AML-M0 and AML-M1. The method used is divided into two stages, first pre-processing, image segmentation and feature extraction. The second stage, perform resample, which is continued over sampling with SMOTE. The process is done until the amount of data obtained is relatively the same for each blast cell, then the process of elimination of data duplication, randomize, classification and performance measurement. The validation method used is k-fold cross-validation with k=10. Performance parameters used are sensitivity, specifyicity, accuracy, and AUC. The average performance resulting from classification of cell types in AML with Random Forest algorithm obtained 82.9% sensitivity, 92.1% specificity, 89.6% accuracy and 87.5% AUC. These results indicate a significant improvement compared to the system model without using SMOTE. The performance generated by reference to the AUC value, the proposed system model belongs to either category, so it can be used for further stages of leukemia subtype AML-M0 and AML-M1

Serum protein fingerprinting by PEA immunoassay coupled with a pattern-recognition algorithms distinguishes MGUS and multiple myeloma

Serum protein fingerprints associated with MGUS and MM and their changes in MM after autologous stem cell transplantation (MM-ASCT, day 100) remain unexplored. Using highly-sensitive Proximity Extension ImmunoAssay on 92 cancer biomarkers (Proseek Multiplex, Olink), enhanced serum levels of Adrenomedullin (ADM, P-corr=.0004), Growth differentiation factor 15 (GDF15, P-corr=.003), and soluble Major histocompatibility complex class I-related chain A (sMICA, P-corr=.023), all prosurvival and chemoprotective factors for myeloma cells, were detected in MM comparing to MGUS. Comparison of MGUS and healthy subjects revealed elevation of angiogenic and antia-poptotic midkine (P-corr=.0007) and downregulation of Transforming growth factor beta 1 (TGFB1, P-corr=.005) in MGUS. Importantly, altered serum pattern was associated with MM-ASCT compared to paired MM at the diagnosis as well as to healthy controls, namely by upregulated B-Cell Activating Factor (sBAFF) (P-corr<.006) and sustained elevation of other pro-tumorigenic factors. In conclusion, the serum fingerprints of MM and MM-ASCT were characteristic by elevated levels of prosurvival and chemoprotective factors for myeloma cells.Web of Science841694216940

From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy

This chapter addresses the following question: What are the advantages of extending a fuzzy expert system (FES) to an artificial neural network (ANN), within a computer‐based speech therapy system (CBST)? We briefly describe the key concepts underlying the principles behind the FES and ANN and their applications in assisted speech therapy. We explain the importance of an intelligent system in order to design an appropriate model for real‐life situations. We present data from 1‐year application of these concepts in the field of assisted speech therapy. Using an artificial intelligent system for improving speech would allow designing a training program for pronunciation, which can be individualized based on specialty needs, previous experiences, and the child\u27s prior therapeutical progress. Neural networks add a great plus value when dealing with data that do not normally match our previous designed pattern. Using an integrated approach that combines FES and ANN allows our system to accomplish three main objectives: (1) develop a personalized therapy program; (2) gradually replace some human expert duties; (3) use “self‐learning” capabilities, a component traditionally reserved for humans. The results demonstrate the viability of the hybrid approach in the context of speech therapy that can be extended when designing similar applications

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes

Diabetes is a serious, chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades. It can lead to serious complications and can increase the overall risk of dying prematurely. Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially. This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes. The R-GRNN Oracle (Bani-Hani, 2017) is an enhancement to the GRNN Oracle developed by Masters et al. in 1998, in which the recursive model is created of two oracles: one within the other. Several classifiers, along with the R-GRNN Oracle and the GRNN Oracle, are applied to the dataset, they are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian NaEF;ve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). Genetic Algorithm (GA) was used for feature selection as well as the hyperparameter optimization of SVM and MLP, and Grid Search (GS) was used to optimize the hyperparameters of KNN and RF. The performance metrics accuracy, AUC, sensitivity, and specificity were recorded for each classifier. The R-GRNN Oracle was able to achieve the highest accuracy, AUC, and sensitivity (81.14%, 86.03%, and 63.80%, respectively), while the optimized MLP had the highest specificity (89.71%)