A Comparative Study of Nine Machine Learning Techniques Used for the Prediction of Diseases

Danielle Azar, Rebecca Moussa, Georges Jreij

Abstract



We present a comparative study of nine state-of-the-art machine learning techniques for classification of diseases. These are: ID3, Deep Learning, Artificial Neural Networks, Naïve Bayes, Logistic Regression, Partial Decision Trees, k-Nearest Neighbor, Classification via Clustering and Voting Feature Intervals. We test these techniques on eight datasets for the classification of different diseases. The data sets vary in their characteristics. We use two modeling techniques: cross-validation and boot-strapping. We assess the machine learning techniques using the following performance metrics: accuracy, precision and area under the ROC curve. Results show that they can be very beneficial over a wide range of diseases.

Keywords


Classification, Disease prediction, Machine learning, Diagnosis.

Refbacks

  • There are currently no refbacks.


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.