Efficient Prediction of Liver Disease using Selected Attributes

2017 ◽  
pp. 10-18
Author(s):  
◽  
◽  
Keyword(s):  
Liver Disease ◽  
Early Stage ◽  
Research Work ◽  
Computation Time ◽  
Vital Role ◽  
Medical Field ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Efficient Prediction ◽  
Early Decision

Liver plays a vital role in the human body that performs several crucial life functions. A number of liver diseases exist and it is a challenging task to diagnose the liver disease at its early stage. In recent years, several data mining techniques have been used in medical field for prediction but there can be further improvements for quick and accurate diagnose of liver disease. In this paper, a variety of Classifiers have been experimented on Indian liver disease patients dataset which is publicly available on Kaggle. Attribute subset selection is performed to identify significant attributes and the resulting dataset is named as Selected Attributes Dataset (SAD). SAD provides more accuracy in less computation time using Random forest classification algorithm and improved system including these parameters i.e., the efficiency of the system can be increased, early decision making, less time and space required. This research work will provide help to predict liver disease with less amount of data, i.e., number of attributes.

scholarly journals Kernel Convolutional Neural Network Deep Learning Algorithm To Classify LIVER Disease

2019 ◽  
Vol 8 (6) ◽  
pp. 2002-2007
Author(s):  
Pardeep Kaur ◽  
Harinder Kaur
Keyword(s):  
Liver Disease ◽  
Learning Algorithm ◽  
Eating Habits ◽  
Kernel Method ◽  
Early Stage ◽  
Research Work ◽  
Disease Diagnosis ◽  
The Body ◽  
Common Disease ◽  
Deep Learning Algorithm

Now a day, liver disease is common disease due to the bad eating habits among individuals. Some disturbance in the functioning of the liver may cause liver sickness. Liver is responsible for overall functioning of the body. Hence, it becomes necessary to diagnosis the liver disease at an early stage. In advanced world of technology, various methods has been been developed to diagnosis and detect the disease includes data mining. This is novel concept to determine the data by extracting features and recognize indications of liver disease by medical experts. The existing technique has implemented optimize the rules released from Boosted classification with a genetic algorithm, to enhance the LDD (Liver Disease Diagnosis) interval of time and accuracy level. Hence, GA is utilized for enhancing and enhancing directions of another method. In this research work, defines a novel method ECNN (Enhanced CNN) of LDD and enable medical specialists to recognize sign of disease and optimization is done for maximum period, decrease the death rate. Clustering and Feature extraction phase to extract the unique feature based on Kernel method and divide the data into a group or cluster-based using FCM algorithm. Implement CNN method to predict or detect the liver disease to improve the performance and classification of rules set. The proposed method has implemented to achieve better performance and compared with existing methods. The simulation tool used in this research works MATLAB 2016a and calculates the performance is Accuracy achieved 96 % ad existing GA accuracy rate 92.9 % achieved in our work

scholarly journals Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

2019 ◽  
Vol 9 (2) ◽  
pp. 2999-3006
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Missing Values ◽  
Data Augmentation ◽  
Research Work ◽  
Reduction Process ◽  
Computation Time ◽  
Activation Function ◽  
Vital Role ◽  
Medical Community

Cancer detecting technology plays a vital role in the medical community. Researches have shown that patients that are affected by cancer carry same type of genetic patterns in their DNA. With this in mind, this research work concentrates on analysing gene pattern for detecting cancer using deep learning algorithms. The Feedback based Adaptive Recurrent Neural Network (FA-RNN) approach is designed to classify and analyse the gene pattern recognition. The data augmentation is done to improve the quality of the input data from COSMIC dataset which includes the detection of missing values, removing the noise present in input using multiple imputations and reducing higher base value can be done using dimensionality reduction process. After obtaining the improved dataset, the training phase begins by estimating the exact weight value of feedback layer using feedback weight loop calculation technique to lessen number of repetition during training. Moreover, the error calculation is done to evaluate the exact weight values of feedback layer used for classification. Finally the classification is done by selecting the next appropriate hidden neuron using the neuron selection activation function. The performance of the Feedback based Adaptive Recurrent Neural Network technique can be analysed using the evaluation metrics accuracy, computation time and Root Mean Square Error (RMSE) and the attained results are compared with the Recursive Neural Network(RNN) and Convolutional Neural Network(CNN) algorithms. The obtained results such as higher accuracy, reduced RMSE and less computation time in Feedback based Adaptive Recurrent Neural Network indicates that it performs the enhanced operation than CNN and RNN.

scholarly journals Loan Amount Prediction Using Machine Learning

2021 ◽  
pp. 212-215
Author(s):  
Bhagyashri Rajesh Jawale ◽  
Priyanka Anil Badgujar ◽  
Rita Dnyaneshwar Talele ◽  
Dr. Dinesh D. Patil
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Research Work ◽  
Ensemble Methods ◽  
Classification Algorithm ◽  
Optimization Techniques ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Or Organization ◽  
Loan Amount

Loan amount prediction is helpful for banks or organization who want their work easier. All Banks give Loan to customer and customer first apply for loan after any bank or organization validate customer information. It must be providing some advantages for banks or company or any organization who wants to give loan. There are various methods to improve the accuracy classification algorithm. The accuracy of random forest classification algorithm can be improved using Ensemble methods. Optimization techniques and Feature selection methods available. In this research work novel hybrid feature selection algorithm using wrapper model and fisher introduced. The main objective of this paper is to prove that new hybrid model produces better accuracy than the traditional random forest algorithm.

A Hybrid Approach for Heart Disease Prediction

2021 ◽  
pp. 95-101
Author(s):  
Aamir Khan ◽  
Dr. Sanjay Jain
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Heart Disease ◽  
Random Forest ◽  
Research Work ◽  
Hybrid Approach ◽  
Disease Prediction ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Analysis Of Performance

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

scholarly journals Automatic Detection of Glaucoma based on Refined Complete Local Binary Pattern and Random Forest Classification Method

2019 ◽  
Vol 8 (10S) ◽  
pp. 15-22
Keyword(s):  
Random Forest ◽  
Local Binary Pattern ◽  
Color Image ◽  
Early Stage ◽  
Automatic Detection ◽  
Classification Method ◽  
Computational Time ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Restoration Technique

Glaucoma is considered to be one of the main root causes of blindness. As it shows no symptoms, if not properly identified at the correct time would result in the loss of vision. This paper proposes a method for the Automatic Detection of Glaucoma based on Refined Complete Local Binary Pattern and Random Forest Classification Method(RCLBP-RFC), which identifies the presence or the absence of glaucoma in a patient at an early stage. The first step is use to convert a color image into gray scale image and the second step we use Neighborhood Fuzzy K Means Clustering to segment Optic Disc(OD) and Optic Cup(OC). In the third step Statistical Optimized and Restoration model is use to extract the enhanced images using the restoration technique. In the Fourth step we exploit Refined Complete Local Binary Patterns Extraction to extract the most relevant features and finally, Random Forest Classification methods are involved to classify the features as normal, abnormal or early detected glaucoma. The experiments show that our RCLBP-RFC method achieves state-of-the-art OD and OC segmentation result on DRIONS dataset. Experimental results indicates that the proposed method identifies the presence or absence of glaucoma more precisely than other existing methods in terms of computational time and complexity, and accuracy

A Computer-based Clinical Information System

1987 ◽  
Vol 26 (04) ◽  
pp. 189-194
Author(s):  
S. S. El-Gamal
Keyword(s):  
Information System ◽  
Hospital Information System ◽  
Clinical Information System ◽  
Research Work ◽  
Clinical Information ◽  
Hospital Staff ◽  
Vital Role ◽  
Daily Routine ◽  
Specialized Hospital ◽  
Computer Based

SummaryModern information technology offers new opportunities for the storage and manipulation of hospital information. A computer-based hospital information system, dedicated to urology and nephrology, was designed and developed in our center. It involves in principle the employment of a program that allows the analysis of non-restricted, non-codified texts for the retrieval and processing of clinical data and its operation by non-computer-specialized hospital staff.This Hospital Information System now plays a vital role in the efficient provision of a good quality service and is used in daily routine and research work in this hospital. This paper describes this specialized Hospital Information System.

The Therapeutic and Diagnostic Value of Fluorine

2020 ◽  
Vol 4 (1) ◽  
pp. 17-29
Author(s):  
Isma Attique ◽  
Shabbir Hussain ◽  
Muhammad Amjad ◽  
Khalida Nazir ◽  
Muhammad Shahid Nazir
Keyword(s):  
Anticancer Agent ◽  
Chemotherapeutic Agent ◽  
Functional Materials ◽  
Diagnostic Value ◽  
Vital Role ◽  
Malignant Growth ◽  
Medical Field ◽  
Active Nucleus ◽  
Broad Application ◽  
Tracer Atom

Fluorine has a useful positron transmitting isotope and it enjoys broad application in the medical field. It is utilized in fluorinated agents,therapeutic sciences and steroid field. Fluorine incorporation viafluoroalkylation is a useful approach in the development of new functional materials and in drug design. Fluorine also plays its role as an anticancer agent and is a successful chemotherapeutic agent for certain sorts of malignant growth. 5-fluorouracil plays a vital role in the treatment of cancer. 18 Facts as a radio label tracer atom in PET imaging. 19 F has the second most sensitive and stable NMR-active nucleus.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Biological and Phytochemical Screening of Fumaria indica extract on Chemically Induced Hepatocellular Carcinoma with Reference to Biochemical Parameters

2019 ◽  
Vol 10 (02) ◽  
Author(s):  
Irfan Aziz ◽  
Birendra Shrivastava ◽  
Chandana Venkateswara Rao2 ◽  
Sadath Ali
Keyword(s):  
Free Radicals ◽  
Liver Disease ◽  
Liver Cancer ◽  
Early Stage ◽  
Food Additives ◽  
Ethanolic Extract ◽  
Hepatoprotective Activity ◽  
Control Group ◽  
Industrial Chemicals ◽  
Toxic Industrial Chemicals

Liver disease or liver cancer is the sixth most common cancer and the third leading cause of cancer mortality in the world. Hepatitis viral infection, food additives, alcohol, fungal toxins (aflatoxins), toxic industrial chemicals, air and water pollutants are the major risk factors of liver cancer. Moreover, due to high tolerance of liver, HCC is seldom detected at an early stage and once detected treatment faces a poor prognosis in most cases.Fumaria indica possesses hepatoprotective activity as evidenced by the significant and dose dependent restoring the activities of entire liver cancer marker enzymes, diminution in tumor incidence, decrease in lipid peroxidation (LPO) and increase in the level of antioxidant enzymes (GSH, CAT, SOD, GPx and GST) through scavenging of free radicals, or by enhancing the activity of antioxidant, which then detoxify free radicals. These factors protect cells from ROS damage in NDEA and CCl4-induced hepatocarcinogenesis. Histopathological observations of liver tissues too correlated with the biochemical observations. Thus, present investigation suggested that the Fumaria indica would exert a chemoprotective effect by reversing the oxidant-antioxidant imbalance during hepatocarcinogenesis induced by NDEA and CCl4. Besides Fumaria indicais very much effective in preventing NDEA-induced multistage hepatocarcinogenesis possibly through antioxidant and antigenotoxic nature, which was confirmed by various liver injury and biochemical tumour markers enzymes. The hepatoprotective activity of a Fumaria indicaof 50 % ethanolic extract was studied using rats. The animals received a single intraperitoneal injection of N-nitrosodiethylamine 200mg/kg body wt followed by subcutaneous injection of CCl4 in a dose of 3 ml/kg body wt. Fumaria indica extract dose dependently and significantly the increase in serum hepatic enzyme levels after NDEAand CCl4 treatment compared to the toxin control group. The results of this study confirmed the antioxidant and hepatoprotective activity of the Fumaria indicaextract against carbon tetrachlorideand N-nitrosodiethylamine induced hepatotoxicity in rats. In addition to this, studies on molecular aspect of hepatoprotective therapy will give mechanistic information in hepatoprotective therapy and also critical balance should be there between the animal model and clinical research. The hepatoprotective properties of Fumaria indicashould provide useful information in the possible application in hepatic liver disease.

Novel Coronavirus SARS-CoV-2 (COVID-19) and Pregnancy: A hypothetical view

2020 ◽  
Vol 20 ◽  
Author(s):  
Ahmed RG
Keyword(s):  
Immune System ◽  
Cellular Therapy ◽  
Early Stage ◽  
Control Strategies ◽  
Vital Role ◽  
Healthy Life ◽  
Global Pandemic ◽  
Maternal Immune System ◽  
Perinatal Management ◽  
Novel Coronavirus

Background: The complications of the SARS-CoV-2 infection and its COVID-19 disease on mothers and their offspring are less known. Objective: The aim of this review was to determine the transmission, severity, complications of SARS-CoV-2 infection during the pregnancy. This review showed the influence of COVID-19 disease on the neonatal neurogenesis. Owing to no specific vaccines or medicines that were reported for the treatment of COVID-19 disease, this review suggested some control strategies like treatments (medicinal plants, antiviral therapy, cellular therapy, and immunotherapy), nutrition uptake, prevention, and recommendations. Discussion: This overview showed in severely states that SARS-CoV-2 infection during the early stage of pregnancy might increase the risk of stress, panic, and anxiety. This disorder can disturb the maternal immune system, and thus causing a neurodevelopmental disturbance. This hypothesis may be depending on the severity and intensity of the SARS-CoV-2 infection during pregnancy. However, vertical transmission of SARS-CoV-2 from dams to their fetuses is absent until now. Conclusion: During this global pandemic disease, maintaining safety during pregnancy, vaginal delivery, and breastfeeding may play a vital role in a healthy life for the offspring. Thus, international and national corporations should be continuing for perinatal management, particularly during the next pandemic or disaster time.

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