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WebMAC: A web based clinical expert system

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Abstract

Disease diagnosis at early stages can enable the physicians to overcome the complications and treat them properly. The diagnosis method plays an important role in disease diagnosis and accuracy of its treatment. A diagnosis expert system can help a great deal in identifying those diseases and describing methods of treatment to be carried out; taking into account the user capability in order to deal and interact with expert system easily and clearly. A good way to improve diagnosis accuracy of expert systems is use of ensemble classifiers. The proposed research presents an expert system using multi-layer classification with enhanced bagging and optimized weighting. The proposed method is named as “M2-BagWeight” which overcomes the limitations of individual as well as other ensemble classifiers. Evaluation of the proposed model is performed on two different liver disease datasets, chronic kidney disease dataset, heart disease dataset, diabetic retinopathy debrecen dataset, breast cancer dataset and primary tumor dataset obtained from UCI public repository. It is clear from the analysis of results that proposed expert system has achieved high classification and prediction accuracy when compared with individual as well as ensemble classifiers. Moreover, an application named “WebMAC” is also developed for practical implementation of proposed model in hospital for diagnostic advice.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets.html [Last Accessed 25 Sep. 2015]

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Authors and Affiliations

  1. Computer Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Saba Bashir, Usman Qamar & Farhan Hassan Khan

Authors
  1. Saba Bashir
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  2. Usman Qamar
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Corresponding author

Correspondence to Usman Qamar.

Appendix: Details of the datasets

Appendix: Details of the datasets

1.1 Datasets

1.1.1 Liver disease datasets

Two liver disease datasets namely Bupa liver disease dataset and ILPD liver disease dataset are used for evaluation purposes. Both datasets are obtained from the UCI machine learning repository. Each dataset contains a diverse set of attributes and instances that will ultimately determine the presence or absence of liver disease in patients. The class labels are represented by 0 and 1 where 0 indicates the absence of disease, whereas 1 represented the presence of disease. Complete description of each dataset is given below:

  1. a)

    Bupa Liver Disease Dataset

The Bupa liver diabetes dataset was initially taken from BUPA Medical Research Ltd. There are 345 instances in the dataset representing both healthy and liver disease patients. There are seven attributes in the dataset containing no missing values. It is a complete dataset containing categorical, real and integer type attributes. The first 5 variables are all blood tests which are thought to be sensitive to liver disorders that might arise from excessive alcohol consumption. A sample of the Bupa liver diabetes dataset is shown in Table 28.

  1. b)

    Indian Liver Patient Dataset (ILPD)

The Indian liver patient dataset was collected from north east of Andhra Pradesh, India. There are 583 instances in the dataset which contains 416 liver patients’ record and 167 non-liver patient’s record. The dataset contains 441 male patients’ record and 142 female patients’ records. There are 10 attributes in the dataset that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos. The dataset does not contain any missing value attribute. The attributes consist of integer and real type data sets. A sample of ILPD dataset is shown in Table 29.

1.1.2 Chronic kidney disease dataset

The chronic kidney disease dataset is used to determine chronic kidney disease in patients. There are two class labels in the dataset; CKD (chronic kidney disease) and NotCKD. The dataset contains 24 disease diagnosis attributes and 1 class label attribute. There are 400 instances in the dataset and it also contains missing values. The CKD patients are 250 and NotCKD are 150. The class labels are replaced with 0 and 1 where 0 indicates NotCKD whereas 1 represent CKD patients. A sample of CKD dataset is given in Table 30.

1.1.3 Cleveland heart disease dataset

There are total 303 records in Cleveland heart disease dataset. The training set is composed of 272 instances, whereas test set consists of 31 instances. The feature space contains 14 attributes where 13 attributes present vital signs and one attribute is goal class (0, 1), 0 presents the absence of heart disease and 1 show the presence of dis- ease. A sample dataset of heart disease from UCI repository is shown in Table 31.

1.1.4 Diabetic retinopathy debrecen dataset

This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. The dataset contains 121 instances and 20 attributes. There is no missing value in the dataset. The class label 1 contains sign of disease whereas 0 indicates absence of disease. A sample set of Diabetic Retinopathy Debrecen dataset is shown in Table 32.

1.1.5 Wisconsin breast cancer dataset (WBC)

The Wisconsin breast cancer dataset consists of 699 instances and 11 attributes. 10 attributes represent feature information, whereas one attribute contains class information where 2 = Benign and 4 = Malignant. There are 16 missing values in the dataset which are denoted by “?”. The class distribution consists of 458 benign instances and 241 malignant instances. This represents an unbalanced dataset. A sample of WBC dataset is shown in Table 33.

1.1.6 Primary tumor dataset

The primary tumor dataset is initially taken from Ljubljana Oncology Institute. The dataset contains 339 instances and 17 attributes. The class label is replaced with either 0 or 1 where 0 is absence of disease and 1 indicates presence of disease. A sample of primary tumor dataset is given in Table 34.

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Bashir, S., Qamar, U. & Khan, F.H. WebMAC: A web based clinical expert system. Inf Syst Front 20, 1135–1151 (2018). https://doi.org/10.1007/s10796-016-9718-y

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