APPLICATION OF GENETIC ALGORITM AND ARTIFICIAL NEURAL NETWORK IN PREDICTING MALARIA AND TYPHOID

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APPLICATION OF GENETIC ALGORITM AND ARTIFICIAL NEURAL NETWORK IN PREDICTING MALARIA AND TYPHOID

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Malaria can be regarded as a life-threatening parasite which is contained in spittle of mosquitoes and is transmitted through a bite (Pirnstill & Coté, 2015; Razzak, 2015). Thereafter biting human, it takes about 45 minutes to spread across entire human blood (Bartoloni & Zammarchi, 2012). Afterward, the infection would start confronting the body’s red blood cells together with liver cells, altering the body’s biochemistry and attributes of cells built – structure (Pirnstill & Coté, 2015). The four most common species of malaria parasites found in SubSaharan Africa includes P.falciparum, P. vivax, P. ovale and malariae ( Calderaro, Piccolo,

Gorrini, Rossi, Montecchini, Dell’Anna, & Arcangeletti, 2013). Among them, P.falciparum was regarded as the most common causes of malaria and its severe cases (Gomes, Vitorino, Costa, Mendonça, Oliveira, & Siqueira-Batista, 2011). Therefore, historically, P.falciparum was attributed to the causes of malaria disease in Sub-Saharan Africa (Howes et al., 2015).

The numerous figures from assessing the hazard of this infection have been overstressed, with first initial score estimates of 300-500 million clinical cases annually leading to 1-3 million deaths globally (Chotivanich, Silamut, & Day, 2007). The parasite of P.falciparum cases of malaria alone was responsible for an approximate estimate of 40% (2.4 billion population) of the world that was affected by malaria (Gomes et al., 2011). With reference to World malaria report of (2010), out of 225 million recorded incidence in the world, 781000 death in 2009 (Mohapatra, Jangid, & Mohanty, 2014), and from a total of 198 million incidences of malaria documented in 2013, 584000 were stated death (Gu, Chen, & Yang, 2015). The most devastating effect of malaria infection is that its most targets are children <5 years of age giving an annual estimate of >300 million population globally and >3000 pediatric per day (Stauffer & Fischer, 2003).

However, many levels of this infection from even clinical point of view were present in patients, in which at some degree may cause neurological complication in human brain called as cerebral malaria (Idro, Marsh, John, & Newton, 2010).

Cerebral malaria can be defined as any anomaly of mental status in a patient with malaria and has a case death rate between 15% and 50%. Cerebral malaria is a quickly progressive possibly lethal complication of P.falciparum infection. It is categorized by unarousable and persistent coma along with regular motor signs. The most vulnerable groups of people are pregnant women, children, and adults with weak immune system. Many scholars agreed to the stated term that the most occurring severity complications of malaria are severe anemia and cerebral malaria. Cerebral malaria is the most evident cause of neurological complication of malaria infection with P.falciparum. Its syndrome is clinically characterized by the presence of a sexual forms of the parasite and coma caused by no any other concomitant disease of these features (Idro et al., 2010).

1.2 Data Mining

Data mining is a process of scrutinizing data from a different viewpoint and collecting the knowledge from such data (Hemanth, Vastrad, & Nagaraju, 2011; Dangare & Apte, 2012). Data mining involves the use of numerous techniques in pattern recognition and knowledge presentation. In fact, data mining task is accomplished by using: Association, class description, classification, prediction, clustering, and time series analysis (Srinivas, Rani & Govrdhan, 2010).

The healthcare industry in this 21st century is rich with data, and this data is an ingredient in data mining and knowledge discovery (Dangare & Apte, 2012). Knowledge discovery is a welldefined process of distinct phases and data mining is the important phase in the discovery of useful hidden knowledge in large databases (Soni, Ansari, Sharma, & Soni, 2011; Srinivas et al., 2010). For example, the huge amount of medical data that are gathered in hospital on daily basis contains hidden information. This hidden information could be extracted using various data mining technique and used for decision making (Taneja, 2013). Data mining provides a mechanism for novelty and discovering of unobserved patterns in data (Srinivas et al., 2010). Data mining can also be referred to as a process of discovering pattern and mining of this pattern from large datasets (Jothi, Rashid, & Husain, 2015).

Data mining has been used in the healthcare sector to provide an assistive tool for early detection, prediction systems of various diseases that can be used for decision making (Jothi et al., 2015). It is sometimes used as the technique of both classification and clustering to achieve a common goal (Patil, Chopade, Mishra, Sane, & Sargar, 2016). Data mining can be useful in answering many vital and critical questions about health care (Srinivas et al., 2010). For example, data mining has been used in prediction of malaria outbreak using a large data set from Maharashtra state, India. The prediction was achieved using two data mining classification Support Vector Machine (SVM) and Artificial Neural Networks (Sharma, Kumar, Panat, & Karajkhede, 2015). It is used to improve quality of service in the healthcare industries and assist the medical practitioner in reducing the number of adverse effect on drug in order to recommend cheap medicinally equivalent substitutes ( Srinivas et al., 2010).

1.3 Classification

Classification is the process of defining a fitting model which describes and differentiates class label with the aim of providing the ability to use the model to predict the class of tuples whose class label is unidentified (Soni, Ansari, Sharma, & Soni, 2011). This imitative model is constructed on the analysis of training data i.e., data tuples whose class label is identified (Tribhuvan, Tribhuvan, & Gade, 2015).

One key area of data mining that demonstrates its application in the healthcare sector is the use of classification techniques in classifying and predicting various diseases (Srinivas et al., 2010). Classification is supervised learning methods that extract models, labeling significant data classes or predicting upcoming trends (Soni et al., 2011). It is the process of assigning a class to find formerly unseen records as correctly as possible by using a collection of records called a training dataset, where each tuple in the training set comprises a set of attributes, and then one of the attributes is called a class. The objective is to provide a classification model for the class elements, then devise a validation mechanism using test data set in order to determine the accuracy of the model ( Kuar & Wasan, 2006). This technique has been used in the healthcare environment to automatically diagnosis a patient’s disease in order to choose immediate treatment while awaiting lab-test results (Nikam, 2015).

Several classification techniques exist in data mining which includes: Support Vector Machine (SVM), K-nearest neighbor, Naive Bayes, IB3, Artificial Neural Network (ANN), Decision Tree and J48, C4.5 version of decision tree classification (Nikam, 2015). Each of these techniques can be used to classify record depends on the nature of pattern in data and the phenomena to investigate ( Kriegel, Borgwardt, Kröger, Pryakhin, Schubert, & Zimek, 2007).

1.4 Problem Statement

The clinical presentation of malaria in a patient is the symptomatic features presented by patients. This feature is an indication of disease course and therefore, has direct significance in guiding clinicians about the decision to take. The combination of symptoms and signs has made tremendous achievement in predicting disease (Lubezky, Ben-Haim, Nakache, Lahat, Blachar, Brazowski, & Klausner, 2010). Even the standard diagnostic criteria developed by clinicians and researchers was based on clinical manifestation that assists with an integrated approach in treatment and management of disease (Laishram, Sutton, Nanda, Sharma, Sobti, Carlton, & Joshi, 2012; Patil et al., 2016).

The main challenging issue confronting the healthcare industry is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly (Chaurasia & Pal, 2013) or administering therapy that is effective, and sometimes even understanding the complications that may result from diseases (Srinivas et al., 2010; Dangare & Apte, 2012). This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable (Dangare & Apte, 2012).

The availability of patients medical data has derived the need for clinicians, payers, and patients for an alternative computer-based assessment tool that can assist in decision making (Soni et al., 2011). For example, the physicians can compare analytical information of numerous patients with the matching condition and physicians can equally confirm their results with the conformity of other physicians dealing with a matching case from another part of the country (Srinivas et al., 2010). For a disease that can be complicated like malaria, the patient is first classified as either have the presence of malaria i.e. positive or negative before further classify the severity of the disease as either uncomplicated (mild) or complicated (severe) based on clinical manifestation (Bartoloni & Zammarchi, 2012). In each case, it’s highly important to understand the clinical features of these classes (Gomes et al., 2011). In the case of classifying positive malaria, the most clinical features are fever, headache, vomiting, and loss of appetite in accordance with the report for predicting malaria (Ndyomugyenyi, Magnussen, & Clarke, 2007). Based on this assumption, we consider these features in predicting positive malaria in phase 1. Further classifications of complicated or uncomplicated are in phase 2. Following are the classes considered in the study:

i. Positive Class (P): Patient can be confirmed to have positive malaria when the patient has one or more of the above symptoms and has also been confirmed by laboratories (Mutanda, Cheruiyot, Hodges, Ayodo, Odero, & John, 2014). Patients found to be positive from diagnosis can confirm the clinical suspicion of malaria (Bartoloni & Zammarchi, 2012). Therefore, in the course of this study, we considered fever, headache, nausea, and vomiting as the most occurring symptoms in a patient with malaria.

ii. Negative Class (N): Patient may have some of the parameters (symptoms) of positive malaria, but after several trying of diagnostic tests to confirm, the malaria is undetectable (Cdc, 2013). This means that the existence of the signs may be as a result of other concomitant disease but not really caused by malaria (Rai & Abraham, 2012).

iii. Mild or Uncomplicated Class (U) of malaria is the presence of one or few signs of clinical manifestation such as mile fever, sweating, weakness, chills, loss of appetite coupled with a headache or recent history of malaria but no signs of severity (Arévalo-Herrera, Lopez-Perez, Medina, Moreno, Gutierrez, & Herrera, 2015). The core central features, in this case, are fever and headache (Rai & Abraham, 2012). iv. Severe or Complicated Class (C) manifest with one or more of the following features; repeated generalized convulsions, impaired consciousness / coma or circulatory collapse/shock, acute respiratory distress syndrome, severe anemia, renal failure, metabolic acidosis, hypoglycemia, hyperthermia, abnormal bleeding and hyperparasitaemia (Bartoloni & Zammarchi, 2012; Laishram et al., 2012). Headache and high fever are centered across all class of suspected patients with malaria (Rai & Abraham, 2012).

1.5 Research Aim & Objectives

The aim of this study is to design a hybrid model for predicting malaria which utilize large data obtained from hospital.

1.5.1 Research Objectives

The major objectives of this research are:

i. To model malaria prediction using Naïve Bayes classifier in order to enhance the accuracy of the prediction model.

ii. To support the understanding of various stages of malaria infection causing complications such as cerebral malaria (malaria which causes edema in a human brain).

1.6 Limitation of the Study

This research study is limited to predicting only malaria and no any other disease. The study highlighted the significance of Naïve Bayes classifier in supervised learning considering its independent assumption. Performance of the system was checked using confusion matrix and ROC only.

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