A Survey on Data Mining Techniques in the Medicative Field

Abstract

Data mining is the process of releasing concealed information from a large set of database and it can help researchers gain both narrative and deep insights of exceptional understanding of large biomedical datasets. Data mining can exhibit new biomedical and healthcare knowledge for clinical decision making. Medical assessment is very important but complicated problem that should be performed efficiently and accurately. The goal of this paper is to discuss the research contributions of data mining to solve the complex problem of Medical diagnosis prediction. This paper also reviews the various techniques along with their pros and cons. Among various data mining techniques, evaluation of classification is widely adopted for supporting medical diagnostic decisions.

Newly Proposed k-NN Method for More Efficient Classification

“…The training process is expected to be very fast and stable even in the presence of noisy data. Furthermore, as with most classifiers, the larger the train dataset, the more effective the classifier would be [9], [15].…”

“…Observations used for training can have big impact on accuracy, so working with representative training dataset is a challenge. Furthermore, the selection ofthe variables for the model should be done with care, as irrelevant features can easily distort the algorithm [9], [15].Importantparameter, which must be selected when initiating k-NN, is the distance (eg. Manhattan, Euclidean or another) the classifier will use to determine the closest points to a new observation.…”

“… F1 score Apart from accuracy, we also use F1 score to evaluate the results of both classic k-NN and Mk-NN(TA) when applied to all datasets. Thus, we are looking how Mk-NN(TA) performs in terms of precision and recall compared to k-NN.The score is computed for all 18 datasets and for each value of k (1,3,5,7,9,11,13,15,30,45, 60 and √n).…”