Evaluation based Approaches for Liver Disease Prediction using Machine Learning Algorithms

Evaluation based Approaches for Liver Disease Prediction using Machine Learning Algorithms

₹5,500.00
Product Code: Python - Machine Learning
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Product Description

Aim:

                  To apply machine learning techniques result in improving the accuracy in the liver disease prediction.

Abstract:

            The life of humans living without liver tumors is one of the fundamental care of human livelihood. Therefore, for better care, detection of liver disease at a primitive phase is necessary. For medical experts, predicting the illness in the early stages due to subtle signs is a very difficult task. Many, when it is too late, the signs become evident. The current work aims to augment the perceive nature of liver disease by means of machine learning methods to solve this epidemic. The key purpose of the present work focused on algorithms for classification of healthy people from liver datasets. Centered on their success variables, this research also aims to compare the classification algorithms and to provide prediction accuracy results.

Introduction:

            The scale of patient medical records increases day by day in the health care sector. Data mining is the method of using a computer-based information system (CBIS), using modern tactics, to uncover insights from data. The machine learning method is close to that of data mining. Algorithms in machine learning can be differentiated from either supervised or unsupervised methods of learning. For statistical modeling, supervised learning approaches are commonly used. Predictive modeling is a subset of the area of clinical and business intelligence that is used to identify health risks and also to forecast individuals' potential health status. In order to store large-scale information on patient outcomes, procedures, etc., electronic health records (EHR) are used. The data on the HER can be organized or unstructured. Electronic health records are stored in a standardized data format using managed language to log patient knowledge as written texts that is hyperlinked in existence. The EHR aims to streamline knowledge about the clinical workflow. Ensemble learning is a well-known method used for prediction by integrating multiple ensemble models of machine learning. Aggregations of various classifiers are. Naive Bayes, etc. Ensembles search for better outcomes than all of the simple classifiers. The proposed work aims to enhance the predictive and classification quality of healthcare data by developing a hybrid predictive classifier model using the classifier ensemble.

Proposed System:

            Our Aim is to predict the Liver disease using the machine learning algorithm. The system is automation for predicting the output. We proposed KNN, DT and Random Forest machine learning technique for liver Disease prediction of significant features. ML process starts from a pre-processing data phase followed by feature selection based on data cleaning, classification of modeling, performance evaluation, and the results with improved accuracy.


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Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
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  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
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  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

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  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.