scholarly journals Implementation of Liver Disease Prediction Using Machine Learning

2021 ◽  
pp. 606-616
Author(s):  
Boggarapu Sai Surya ◽  
Nitesh Kumar Singh ◽  
S Sasi Rekha
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Disease Prediction ◽  
Medical Field ◽  
Data Set ◽  
Classification Techniques

This work Liver Disease Prediction Using Machine Learning is a machine learning application. In this project, you predict whether the patient contain a liver disease or not using python Jupyter Notebook. To predict presence of liver disease we apply some of the classification techniques. It gives an idea of how machine learning helps in medical field and how classification techniques going to predict liver disease using liver disease data set.

Heat Disease Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 846-852
Author(s):  
Prof. Dr. R. Sandhiya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Assessment Process ◽  
Disease Prediction ◽  
Test Results ◽  
Medical Field ◽  
Modern Age ◽  
Linear Svm

In recent times, the diagnosis of heart disease has become a very critical task in the medical field. In the modern age, one person dies every minute due to heart disease. Data science has an important role in processing big amounts of data in the field of health sciences. Since the diagnosis of heart disease is a complex task, the assessment process should be automated to avoid the risks associated with it and alert the patient in advance. This paper uses the heart disease dataset available in the UCI Machine Learning Repository. The proposed work assesses the risk of heart disease in a patient by applying various data mining methods such as Naive Bayes, Decision Tree, KNN, Linear SVM, RBF SVM, Gaussian Process, Neural Network, Adabost, QDA and Random Forest. This paper provides a comparative study by analyzing the performance of various machine learning algorithms. Test results confirm that the KNN algorithm achieved the highest 97% accuracy compared to other implemented ML algorithms.

scholarly journals Prediction of Liver Disease using Regression Tree

2021 ◽  
Vol 17 (02) ◽  
pp. 164
Author(s):  
Vinutha M.R. ◽  
Chandrika J.
Keyword(s):  
Machine Learning ◽  
Liver Cirrhosis ◽  
Liver Disease ◽  
Decision Trees ◽  
Regression Tree ◽  
Decisive Role ◽  
Integrated System ◽  
Common Disease ◽  
Data Set ◽  
Fully Integrated

<p class="0abstract"><strong>Abstract—</strong><strong> </strong>Data Mining plays a decisive role especially in medical domain. Decision trees are predominant model in machine learning. Decision trees are simple and very effective classification approach. The decision tree identifies the utmost prime features of a given problem. One of the most common disease in India is Liver Cirrhosis. It is distinctly difficult to uncover Liver Cirrhosis in its initial stage. However early diagnosis of Liver Cirrhosis is highly important.The liver disease data set has a collection of distinguishing features that affect the healthy state of a patient. Machine Learning methods enable knowledge acquisition in early stages and use of this acquired knowledge plays an important role in solving problems like suppose if we want to predict whether the patient with the Liver Cirrhosis has also been suffering from Hepatitis C or not. In order to easily arrive at this knowledge certainly there is a need for fully integrated system. In this paper the collected Liver disease data set is analyzed and prognosticated whether the patient is suffering from liver cirrhosis or not.</p><p class="0abstract"> </p>

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

scholarly journals Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach

2021 ◽  
Vol 10 (9) ◽  
pp. 67-72
Author(s):  
Talasila Bhanuteja ◽  
◽  
Kilaru Venkata Narendra Kumar ◽  
Kolli Sai Poornachand ◽  
Chennupati Ashish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Local Area ◽  
Learning Approach ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Disease Forecast

The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and LightGBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.

Analysis of Machine Learning Classification Techniques for Anomaly Detection with NSL-KDD Data Set

2021 ◽  
pp. 258-267
Author(s):  
Ana Cholakoska ◽  
Martina Shushlevska ◽  
Zdravko Todorov ◽  
Danijela Efnusheva
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Data Set ◽  
Classification Techniques ◽  
Machine Learning Classification

scholarly journals Accurate Liver Disease Prediction with Extreme Gradient Boosting

2019 ◽  
Vol 8 (6) ◽  
pp. 2288-2295 ◽  
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Classification Accuracy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Sets ◽  
Extreme Gradient Boosting ◽  
C4.5 Decision Tree ◽  
The Times

Abstract-Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 100% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.

Principal Integrant Analysis Based Liver Disease Prediction Using Machine Learning

2021 ◽  
pp. 71-80
Author(s):  
M. Shyamala Devi ◽  
Kamma Rahul ◽  
Ambati Aaryani Chowdary ◽  
Jampani Sai Monisha Chowday ◽  
Satheesh Manubolu
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Disease Prediction

Heart Disease Prediction System Using Ensemble of Machine Learning Algorithms

2019 ◽  
Vol 13 ◽  
Author(s):  
Nandhini Abirami R. ◽  
Durai Raj Vincent
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithms ◽  
Medical Intervention ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Prediction System ◽  
Random Forest Algorithm ◽  
Medical Field

Background: Diagnosing diseases is an intricate job in medical field. Machine learning when applied to health care is capable of early detection of disease which would aid to provide early medical intervention. In heart disease prediction, machine learning techniques have played a significant role. Analysis of disease has become vital in health care sectors. The massive data collected by healthcare sectors are preprocessed and analyzed to discover the underlying information in the data for effective decision making and to provide proper medical intervention. The success of machine learning in medical industry is its capability in analyzing the huge amount of data gathered by the health sector and its effectiveness in decision making. Since medical field involves too many manual processes it has become necessary to automate these procedures. Remarkable advancements in electronic medical records have made it possible. Diagnosing diseases is an intricate job in medical field. Objective: The objective of this research is to design a robust machine learning algorithm to predict heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. Method: Heart Disease Prediction System is developed where the user can input the patient details and the prediction for the particular patient is made using the model developed. The model will predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes classifier are used as base learners. These algorithms are combined using random forest as the meta classifier. Results: The predictions of classifier are combined using random forest algorithm. The accuracy is lifted from 85.53% to 87.64% which is an impressive improvement on accuracy. Conclusion: Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made to optimize the performance of machine learning algorithms. Ensemble prediction gave better accuracy when combined using Random forest algorithm as combiner. Better feature selection techniques can be applied to further improve the accuracy.

Sign in / Sign up

Export Citation Format

Share Document