In the second issue, the problem is supporting machine to understand these knowledge. In order to suggest the essential symptoms based on the input symp-toms, a method following rule-based approach is frequently used with the role definition is manually defined by humans. In a small database, this approach is extremely efficient under the control of experts in designing the policies of rela-tionships among entities. However, the scalability of the database which can be an obstacle for methods in this approach. Due to the manual designing policies in a database, there is an increase in complexity when we want to apply in a large system. Therefore, another method based on queries to a database which is frequently used. This method is to exploit the relations between entities of se-mantic databases such as an ontology for finding the highest probability disease, then suggest a symptom in this disease. However, this method has a disadvantage about weights between diseases and symptoms which can be determined as the probability of having diseases if they have symptoms. Specifically, assume that the given input symptoms include S1 and S2 where S1 is a symptom of disease D1 and S2 belongs to D2, the outcome is undefined because of the equivalent probability between these diseases.
Therefore, I proposed a new method which covers the weight of relationship between diseases and symptoms to suggest the necessary subsequent symptom based on the input symptom. For definition, weight can be considered as a probability of having disease with the input symptoms. Based on a comparison of weights between candidate diseases, the efficient of the proposed method is better than previous methods.
2.4 Disease classification
In recent years, machine learning played an important role in medical data anal-ysis for improving the quality of treatment to patients. There are many serious infectious diseases which were detected by the algorithms in Machine learning. In the last two decades, cancer, diabetes, liver, hepatitis and dengue are five popular diseases which have attracted a lot of attention from researchers. Based on the clinical symptoms and the information of data, the researchers have utilized the classification techniques to separate the patients and healthy people in place of doctors. In terms of the result, the output of the classification algorithms is the binary value which represents the state of patients with diseases (0 = False, 1
2.4 Disease classification
= True). The common algorithms are Artificial Neural Network (ANN), Nave Bayes, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree, and Random Forest. However, ANN is used less than other traditional algo-rithms because this algorithm frequently requires huge data in order to get high accuracy. Meanwhile, the data of patients are usually manually stored as hard copies and the cost of digital converting is also expensive. Therefore, the model with other traditional algorithms are often used to predict the problem of disease classification with clinical symptoms.
For example, in order to detect heart disease [48],[49],[50], [51], Otoom et al.
[49] introduced a system for Coronary artery disease detection and monitor. With the Cleveland heart dataset [52] from UCI Machine Learning Repository, they made an experiment with three algorithms Bayes Net, Support Vector Machine and Functional Tree to optimize the classification of disease. The best result in their experiment is the accuracy of 88.3% by SVM method.
In diabetes classification [53], [54], [55], [56], Iyer et al. [54] made an exper-iment with Pima Indians Diabetes Database of National Institute of Diabetes and Digestive and Kidney Disease. In their experiment, Decision Tree and Nave Bayes algorithms were applied to predict the state of patients with diabetes. They achieved 79.5652% correctness by Nave Bayes algorithm.
For liver disease, Vijayarani and Dhayanand [57] utilized Support Vector Ma-chine and Nave Bayes for classification. Indian Liver Patient Dataset(ILPD) data set is obtained from UCI. Dataset comprises of 560 instances and 10 attributes.
They utilized Matlab to implement Nave Bayes and SVM for liver disease pre-diction.
Ba-Alwi and Hintaya [58] utilized many data mining algorithms to predict hepatitis disease. The comparison among Naive Bayes, Naive Bayes updatable, FT Tree, K Star, J48, LMT, and Neural Network in hepatitis classification is performed by the Weka tool [59]. In the experiment, Support Vector Machine algorithm achieved the best result.
In dengue classification [60], [61], [62], [63], [64], [65], [66], there are many researchers attempt to apply the traditional algorithms for detecting the patients with dengue. For example, a logistic regression algorithm is utilized by Tuan et al. [61] on the data of 5726 children within 72 hours of fever onset to distinguish patients with dengue illness. This dataset includes 35 attributes/features. 19
2.4 Disease classification
features are used out of 35 features. Weka tool was also used for detection. The result of this paper demonstrates the efficiency of statistical methods as a tool for doctors in dengue diagnosis. Thus, the quality of treatment for dengue patients was improved.
Besides, another study by Tanner et al. [62] conducted an experiment using a decision tree algorithm on the data of 1200 patients with fever for the first three days to distinguish patients with dengue illness. In their experiment, compared to the logistic regression algorithm, the decision tree algorithm had the advantage in handling missing values which are commonly encountered in clinical studies.
However, the previous studies often disregard the influence of processing of feature extraction which is one of the important factors to improve the accuracy of classification algorithms. Therefore, we propose a novel approach which aims to find the hidden subspaces for increasing the accuracy of dengue classification.
This approach based on the reality of making a diagnosis from doctors. They are not required to investigate all symptoms to make a diagnosis. Instead, they can make a precise diagnosis based on only several crucial symptoms. By successful detecting a small group of dengue patients, the accuracy of dengue classification is also optimized.
2.4 Disease classification