ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis

ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis

Amit Kumar (Sanaka Educational Trust's Group of Institutions, India), Manish Kumar (Vellore Institute of Technology, Chennai, India) and Nidhya R. (Madanapalle Institute of Technology & Science, India)
DOI: 10.4018/978-1-7998-2742-9.ch026
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Abstract

In recent years, a huge increase in the demand of medically related data is reported. Due to this, research in medical disease diagnosis has emerged as one of the most demanding research domains. The research reported in this chapter is based on developing an ACO (ant colony optimization)-based Bayesian hybrid prediction model for medical disease diagnosis. The proposed model is presented in two phases. In the first phase, the authors deal with feature selection by using the application of a nature-inspired algorithm known as ACO. In the second phase, they use the obtained feature subset as input for the naïve Bayes (NB) classifier for enhancing the classification performances over medical domain data sets. They have considered 12 datasets from different organizations for experimental purpose. The experimental analysis advocates the superiority of the presented model in dealing with medical data for disease prediction and diagnosis.
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Introduction

In recent years, the world has encountered huge challenges with respect to early detection and prediction of diseases (Ilayaraja & Meyyappan, 2013). A useful matching pattern, related information or information extraction is not at all a simple task. Due to unavailability of related tools and technique, the world is facing problem in medical field. As the medical data belong to natural domain, therefore there is a possibility for the dataset for being imbalance, impure and may have vagueness. Due to above mentioned points and due to less information available in the literature, it is quite difficult to develop a disease prediction model for predicting the diseases (Matsumoto et.al. 2002).

Key Terms in this Chapter

Medical Technology: It is a broad field where innovation plays a crucial role in sustaining health. Areas like biotechnology, pharmaceuticals, AI, ML, Cloud Computing, IoT, information technology, the development of medical devices and equipment, and more have made significant contributions to improving the health of people across the globe.

Medical Disease Diagnosis: It is a method of determining the disease by analyzing the symptoms from which a person is suffering.

Feature Selection: It is a method of filtering the valid attributes or elements by discarding the irrelevant or redundant data.

Classifiers: A classifier is a machine-based learning methodology that can be used to discriminate different objects based on certain useful features.

Data Mining: It is a technique used to turn raw data into some useful information. By using software to look for patterns in large batches of data, companies can learn more about their customers to develop more effective marketing strategies, in order to increase sales and decrease costs.

Naïve Bayes: A Naive Bayes classifier is a probabilistic machine learning model that is used for classification tasks.

Ant Colony Optimization (ACO): It is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs by the ants.

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