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Disease Detection Using ML/AI

EasyChair Preprint no. 7429

24 pagesDate: February 7, 2022


Now a days’ different type of diseases is the major reason for increasing mortality rate among us. Manual diagnosis takes a huge time and effort. To reduce the time and effort, it is now become important to develop automatic diagnosis system, disease detector for early detection of different types of disease. Data mining technique is the one which contribute a lot in development of such system. This paper is relative study on various machine learning models which are Support Vector Machine (SVM), Logistic Regression, Decision Tree, Naïve Bayes, MLP, Random Forest, K Nearest Neighbour (KNN), XG-Boost etc. These are done on the dataset taken from UCI repository, Kaggle and many other Platform. With respect to the result of accuracy, precision, recall, sensitivity, specificity, false positive rate, and True positive rate the efficiency of each algorithm is measured and compared. The aim is to analysis of diseases by machine learning classifier for sufficient decision making in health care. The aim of this paper is explanation of Machine learning, & different type of classifiers & their comparison.

Keyphrases: Artificial Intelligence, Diseases, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rukhsun Ara Parvin},
  title = {Disease Detection Using ML/AI},
  howpublished = {EasyChair Preprint no. 7429},

  year = {EasyChair, 2022}}
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