Liver Cancer Identification Grid Search RFC Model using Machine Learning

Liver is essential to the body's digestion of sugar and fats, absorption, and immunological system. This substance is present in almost everything a man takes in, breathes, or absorbs through his skin. Liver disorders are a significant health burden. It is increasing daily and is difficult to detect in its early stages since the liver may function normally even when partially damaged. Doctors have widely employed machine learning algorithms to diagnose liver illness in order to increase the efficiency of medical diagnosis. The study's primary aim is to evaluate how machine learning algorithms may be used to prevent postponing medical care, accurately diagnose liver illness, and minimize the number of erroneous diagnoses provided to sick patients. The main objective is to ensure that liver patients receive an accurate diagnosis as soon as possible

Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis

The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to  show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm

Diagnosing Hepatitis Using Hybrid Fuzzy-CBR

The Malaysia populations are currently estimated to be 28.9 million with a number of medical specialists is 2,500 and 20,280 doctors. This ratio figures to cause patients need to wait longer in government hospitals and clinics before they can meet doctor or medical specialist. In order to resolve this problem, Ministry of Health has pledged to reduce waiting time of patient examination from 45 minutes to 30 minutes by provide allocation of large budget to the medical sector. This budget will be used either to buy new equipment, which can work with large capacity or upgrade the old equipment to work faster or build the new hospital to tend more patients or hire other doctors from overseas. Due to that reason and the coming which World Hepatitis Day on 28 July 2012, this study proposes a the use of hybrid intelligent, which combine Fuzzy Logic and Case-Based Reasoning (CBR) approach that could be integrated in the diagnosis system to classify patient condition by using fuzzy technique and similarity measurement based on current symptoms of a hepatitis patient. Focus of this study is to develop an automated decision support system that can be used by the doctors to accelerate diagnosis processing. As a result, a prototype called Intelligent Medical Decision Support System (IMDSS) using Fuzzy-CBR engine for diagnosis purposes has been developed, validated and evaluated in this study. The finding through validation and evaluation phase indicates that IMDSS is reliable in assisting doctors during the diagnosis process. In fact, the diagnosis of a patient has become easier than the manual process and easy to use

Analysis and Design of Detection for Liver Cancer using Particle Swarm Optimization and Decision Tree

Liver cancer is taken as a major cause of death all over the world. According to WHO (World Health Organization) every year 9.6 million peoples are died due to cancer worldwide. It is one of the eighth most leading causes of death in women and fifth in men as reported by the American Cancer Society. The number of death rate due to cancer is projected to increase by45 percent in between 2008 to 2030. The most common cancers are lung, breast, and liver, colorectal. Approximately 7, 82,000 peoples are died due to liver cancer each year. The most efficient way to decrease the death rate cause of liver cancer is to treat the diseases in the initial stage. Early treatment depends upon the early diagnosis, which depends on reliable diagnosis methods. CT imaging is one of the most common and important technique and it acts as an imaging tool for evaluating the patients with intuition of liver cancer. The diagnosis of liver cancer has historically been made manually by a skilled radiologist, who relied on their expertise and personal judgement to reach a conclusion. The main objective of this paper is to develop the automatic methods based on machine learning approach for accurate detection of liver cancer in order to help radiologists in the clinical practice. The paper primary contribution to the process of liver cancer lesion classification and automatic detection for clinical diagnosis. For the purpose of detecting liver cancer lesions, the best approaches based on PSO and DPSO have been given. With the help of the C4.5 decision tree classifier, wavelet-based statistical and morphological features were retrieved and categorised

Case-Based Reasoning Applied to Medical Diagnosis and Treatment

The Case-Based Reasoning (CBR) is an appropriate methodology to apply in diagnosis and treatment. Research in CBR is growing and there are shortcomings, especially in the adaptation mechanism. In this paper, besides presenting a methodological review of the technology applied to the diagnostics and health sector published in recent years, a new proposal is presented to improve the adaptation stage. This proposal is focused on preparing the data to create association rules that help to reduce the number of cases and facilitate learning adaptation rules

Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis

The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm

Comparative Study of the Symptoms of Impending Huma n Heart, Kidney and Liver Failures Based on Blood Sam ples

While all the organs of the human body works togeth er for the proper functioning of the entire human s ystem, the significant roles played by the heart, kidney and l iver in the lives of humans cannot be overemphasize d. The heart, kidney and liver shares one thing in common, namely: the blood from where several parameters that defines the sta te of human health can be deduced or inferred. This paper is on the compar ative study of the possible symptoms of impending h uman heart, kidney and liver failures based on blood samples that coul d be used to infer the state of human health with a view for the development of an online real-time electronic health (e-health) monitoring system. The comparative study considere d in this work identified 19 vital measurable blood-related parameters that c an be classified as follows: 1). Heart: heart beat, mean arterial blood pressure, systolic blood pressure, diastolic blood pressure; 2). Kidney: packed cell volume/blood cells, erythropoietin, electrolyte Na + , electrolyte Cl - , electrolyte Ca 2+ , vitamin D; and 3). Liver: Ceratine, glucose fasting, glucose random, urea, direct bilirubin, total bilirubim (direct and indir ect), ammonia level, alpha-feto protein. Detailed d iscussion on the functions, tests and diagnosis of each of heart, kidney and li ver as well as the causes, symptoms, failures, cons equences (acute or chronic adverse effects) are also presented. Finally, some preliminary medical advice and suggestions on possi ble ways to circumvent, reduce and/or manage any impending symptoms of fail ures of the heart, kidney and/or liver based on the nominal values as well as the minimum and maximum values of the menti oned 19 parameters are given