A Survey on Machine Learning Techniques For the Diagnosis of Liver Disease

Project Code :TCMAPY500

Objective

The objective of this paper is to give a survey and comparative analysis of the entire machine learning techniques for diagnosis and prediction of liver disease in the medical area.

Abstract

Suffering from liver disease has been rapidly increasing due to excessive drink of alcohol, inhale polluted gas, drugs, contamination food and packing food pickle, so the medical expert system will help a doctor to automatic prediction. With the repeated development in machine learning technology, early prediction of liver disease is possible so that people can easily diagnosis the deadly disease in the early stage. This will give more useful in the Healthcare department and also a medical expert system can be used in a remote area. The liver plays a very important role in life which supports the removal of toxins from the body. So early prediction is very important to diagnosis the disease and recovers. Different types of machine learning algorithms are used for diagnosis of liver disease such as Random Forest, KNN, Logistic Regression, Decision tree etc and give difference accuracy, precision, sensitivity.

KEYWORDS: Supervised learning, Machine Learning, Liver Diagnosis.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:

  • Processor- I3/Intel Processor
  •  RAM- 4GB (min)
  • Hard Disk- 128 GB
  • Key Board-Standard Window
  •  Keyboard. Mouse-Two or Three Button Mouse.
  • Monitor-Any.

SOFTWARE SPECIFICATIONS:

  • Operating System: Windows 7+
  • Technology: Python 3.6+
  •  IDE: PyCharm IDE
  •  Libraries Used: Pandas, NumPy, Scikit-Learn, Matplotlib.

Learning Outcomes

  • About Python.
  • About PyCharm.
  • About Pandas.
  • About Numpy.
  • About HTML.
  • About CSS.
  • About JavaScript.
  • About Database.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • Cloud Overview.
  • Terminology of cloud.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

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