Disease Prediction Using Weighted Artificial Immune System

Günay, Melike
Orman, Zeynep
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The Artificial Immune System (AIS) is a computational intelligence method inspired from the human immune system, which is applied to real-world problem solving related to classification, optimization and anomaly detection as an alternative approach to many data mining techniques. This paper presents a medical disease prediction system by using the AIS algorithm. The proposed system is implemented and tested on two different datasets which include breast cancer data and heart disease data with four different types of illness. Two other well-known data mining techniques that are Artificial Neural Networks (ANN) and K-Nearest Neighbor (KNN) are also tested on the same datasets to make a comparison in terms of their classification efficiency. By using AIS, accuracy obtained on breast cancer dataset is 98.08% and heart disease dataset is 70%. In addition to this, AIS algorithm gives the best classification results for both datasets. We also analyze the positive effect of preprocessing data before classification. Clearly, decreasing the number of different values that a class can be assigned for multivariate classes and assigning weights to each feature in heart disease dataset give prediction result with higher accuracy.

Computing Methodologies , Machine Learning , Learning Paradigms , Bilgi İşlem Metodolojileri , Makine Öğrenme , Öğrenme Paradigmaları