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Early Prediction Of Erythemato-Squamous Disease With Machine and Deep Learning Approaches

EasyChair Preprint no. 4868

13 pagesDate: January 5, 2021


Now a days skin diseases are a major health problems among the many common people.The classification and recognition systems have been improved in a great deal to help in the medical experts in diagnosing diseases.Here we develop the different machine leraning techniques,which can diagnose erythemato-squamous disease.The machine learning techniques applied to skin diseases prediction so far has better outcome over all the others.Here we apply five different machine learning techniques and then develop an ensamble approach that consist of all the five different machine leaning techniques as a single unit.We use informative Dermatology data to analysis different data mining techniques to classify the skin disease and then, an ensemble machine learning method is applied.This study has focused on detection of erythemato-squamous on the dermatology dataset using different machine learning predictive techniques such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF),Linear Regression,Decision Tree,AdaBoost Classifier

Keyphrases: Adaboost classifier, Decision Tree, Dermatology dataset, Erythemato-Squamous Disease, linear regression, Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM)

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Harshvardhan Tiwari and Preeti V Patil and K R Sinchana and Shiji K Shridhar and G Aishwarya},
  title = {Early Prediction Of Erythemato-Squamous Disease With Machine and Deep Learning Approaches},
  howpublished = {EasyChair Preprint no. 4868},

  year = {EasyChair, 2021}}
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