Developing Diabetes Ketoacidosis Prediction using ANFIS Model Galuh Wilujeng Saraswati1, Yun-Huoy Choo2, Yogan Jaya Kumar3 Computational Intelligence & Technologies (CIT) Lab Faculty of Information and Communication Technology University Teknikal Malaysia Melaka, Melaka, Malaysia 1
[email protected],
[email protected],
[email protected]
Abstract— This paper proposes the adaptive Neuro-fuzzy Inference System (ANFIS) to construct a diabetic ketoacidosis prediction model. Diabetic ketoacidosis results in large amount of ketones can be detected in urine through distinctive odour of acetone. Hence, urine odour analysis is able to diagnose diabetic ketoacidosis facilitating dianogstic test for diabetic patient. The Electrical Nose (E-nose) system consists of four metal oxide gas sensors was used to extract urine odour. Common process of diabetic diagnosis require patient to provide fasting urine for more accurate detection. Our work has shown both fasting and nonfasting urine are able to produce good diabetic detection accuracy through urine odour analysis. A total of 40 human subjects from CITO Laboratory, Semarang Central Java, Indonesia, involving 20 diabetic patients and 20 healthy subjects were used to build the prediction model. The proposed model has achieved at least 63% average accuracy in discriminating diabetic patient from healthy subject. When data preprocessing to average the training data samples was implemented, the detection performance was increased to above 93%. The findings have shown promising results of using both fasting and non-fasting data samples for diabetic prediction. This is essential towards the flexibility of diabetic dianogstic test where it does not require patient to fast, therefore can be tested at anytime anywhere. Keywords—Diabetes Analysis, ANFIS modelling, Ketoacidosis, E-nose, odour analysis.
I. INTRODUCTION Diabetes is one of the metabolic diseases groups which has the characteristics of increased blood glucose level as hyperglycemia. World Health Organization (WHO) reported that at least 171 million people are suffering from diabetes. The number will increase by 2 folds by year 2030. Additionally, this disease is one of the biggest killers in Southeast Asia and the Western Pacific [1]. In Indonesia, the Health Ministry of Republic Indonesia reported the diabetes as a public concern ever since Indonesia becomes the top fourth diabetes rate country in the world, with the prevalence of diabetes at 0.6% of people at the aged of 15 years. Diabetes dianogstic test is important to detect new cases as well as playing an significant role for diabetic patient in monitoring and controlling their condition. The diabetes can be detected using both invasive and non-invasive techniques. Blood test is the widely acceptable invasive test used in most of the clinics and hospitals till today. The rise of nanotechnology-based sensors allow glucose monitoring using non-invasive techniques such as urine [2-3], human serum [4], saliva, sweat, breath, and tears [5] to detect
diabetes. These non-invasive techniques have gained recognition as a viable alternative technique for early detection of diabetes from time to time. Nowadays, blood sugar and urine tests are still among the most commonly used diabetes dianogstic tests, which produce reliable clinical results. However, these tests usually encourage patient to fast for 8 to 12 hours before collecting test sample to yield better test accuracy. This limitation causes inconvenience to patient, especially those require regular monitoring. Diabetic ketoacidosis is characterized by ketosis (elevated levels of ketone in the blood) and acidosis (increased blood acidity). Diabetic ketoacidosis is a complication of diabetes mellitus that occurs when human body cannot produce sufficient insulin to convert glucose into energy. Instead, body fat is used as energy source which produce ketone as a byproduct. This severe condition is known as ketoacidosis. High level of ketone can be very dangerous and become toxic to human body. Ketone has a small molecular structure that can be excreted into urine and resulted ketonuria. Ketones found in urine are mainly consist of acetone and acetoacetic acid [2][3]. Large amount of ketones [6] can be detected in urine through distinctive odour of acetone. Therefore, monitoring the acetone and acetoacetic acid levels in urine based on volatile organic compound (VOC) is able to keep track of diabetic conditions. This paper aims to propose an adaptive prediction model capable to diagnose diabetic ketoacidosis through urine odor. The rest of the paper is organized as follows: Section II describe the electronic nose system used for data acquisition. Section III illustrates the experimental design and diabetic ketoacidosis modelling using Neuro-fuzzy Inference System (ANFIS). Section IV depicts the experimental results and analysis while section V draws the conclusion and the direction of future work. II. THE ELECTRONIC NOSE (E-NOSE) SYSTEM The electronic nose, also known as E-nose are used in odour analysis research to detect and identiy specific componets of chemical composition in many sector such as industry, agriculture, environment monitoring and medical application [710]. Research in E-nose, particularly based on volatile organic compound (VOC) has reported better effectiveness, hence it becomes an promising alternative to clinical applications. The E-nose system used in this study was constructed using four metal oxide gas sensors [11] to extract urine odour. The gas
sensor array system was designed to specifically detect volatile profiles of the acetone and acetoacetic acid odor released from urine samples. The system consists of mechanical, electronic and software program components as shown in Fig. 1.
tool (PLX-DAQ) with Excel add-ins. Arduino works to calculate the four response features for each metal–oxide–semiconductor (MOS) sensor. The USART (Universal Synchronous Asynchronous Receiver / Transmitter) and standard FPDI (Flat Panel Display Interface) were used to facilitate communication through a computer's serial port with the data transfer rate at 9600 baud. A graphical user interface was developed using MATLAB R2014b to configure the sensor, download and process the data. III. DIABETES KETOACIDOSIS MODELLEING The proposed diabetes ketoacidosis model aims to clasifiy the concentration of acetone in urine samples from the healty subjects or diabetic patients. The proposed experimental design is as shown in Fig. 2. It consists of six stages. First stage is the urine samples preparation, followed by the selection of data acquisition sensors, data retrieval, implementation of experiment 1 and experiment 2, and lastly the findings and conclusion. Sample Preparation 20 urine diabetes fasting. 20 urine diabetes non-fasting. 20 urine normal fasting. 20 urine normal non-fasting
Fig. 1 Schematic Diagram of the arrangement of the four block used for making the gas sensor chamber
The mechanical component has a gas delivery system to transport the volatiles from the headspace of the sample to the MQS sensor mounted inside a chamber. The selection of sensors is based on the urine gas discard, which can discriminate diabetics patient from healthy patient. The chemical molecular structure of Ketonacidosis is CH3COC2H5. Hence, gas sensors that can detect hydrogen, i-butane, propane, and methane are installed. Besides, human urine contains natural gas with 95% of water. The design of the system hardware starts from the electronic module of the gas sensor array and the electronic module of the acquisition system. The electronic component includes a circuit board with Arduino microcontroller, an inverter, a regulator and other peripheral devices. Software programs were developed to control the Arduino microcontroller and interfacing with the computer system. At the Arduino microcontroller level, a program was developed in data acquisition (DAQ) system using Parallax Data Acquisition
Fig. 2 The Experimental Design Flowchart
A. Preparing Urine Samples Participants in this study are patients engage with clinical laboratory of CITO Semarang. All subjects are either known as diabetic patient and healthy subject. A total of 40 subjects, consists of 20 diabetic patients and 20 normal patients were recruited voluntarily to assist in developing the case study. The
B. Selecting Data Acquisition Sensors Human urine contains many other volatile organic substances, including the acetone and acetoacetic acid, besides ammonia. The strength of acetone odour in urine will rise with the increase of its concentration in urine. Urine volatiles released by diabetic patient mainly contain nitrogen compounds and others chemical groups [6] such as methane, ethane, propane and butane. The selection on MOS sensors was primarily based on chemical specificity and sensitivity. The Arduino board connected to the MOS sensors aims to convert the analogue signals to digital signals with 10 bit resolution. C. Retrieving and Exploration on Urine Odour Data In the e-nose system, urine samples were placed in a dedicated chamber with the existence of sensor array. The fan helped to draw the urine sample odour towards the sensor array to be converted to digital signal by the e-nose system. Data acquisition was set to 10 second for each sample. Next, the acquired data were saved in tabular form according to the behavior of the specified sensor as shown in Table I.
Sensor 1 2 3 4
URINE DIABETES 1100 1000 900 800
Sensitivity (ppm) Rs (in air)/ Rs (1000ppm isobutane) ≥ 5 LPG, LNG Natural gas, isobutane, propane Town gas LPG, isobutane, propane, LNG Hydrogen (H2)
600
400 300 200
0
100
200
300
400
500 600 Time(s)
700
800
900
1000
Fig. 3 Sensor Response on Urine Samples from Diabetic Patient URINE NORMAL 800
THE MOS SENSORS USED TO BUILD GAS SENSOR ARRAY
Specificity LPG, Propane and Hydrogen LPG, natural gas and town gas LPG, isobutane, propane Hydrogen
700
500
700 600
Tegangan(V)
TABLE I.
classification results were then validated with the clinical test results from the laboratory. Both fasting and non-fasting data samples were randomly selected as the training data and testing data. The 5-fold cross validation approach was used to evaluate the ANFIS models performance based on classification accuracy, precision, and recall.
Tegangan(V)
urine samples preparation involves two stages. Every participant is requested to provide his/her urine sample after fasting for at least 8 hours. Next, another urine sample will be collected 2 hours after meals. The true output were determined using the standard microscopic validation procedure commonly used in clinical laboratories to determine the level of glucose in the human body.
500 400 300 200
Fig 3 and 4 show the response of each gas sensor related to the concentration of acetone and acetoacetic acid in urine samples for a diabetic patient and a healthy subject respectively. The urine contents in diabetics patient showed obviously lower concentration in hydrogen detected in sensor 4. Sensor 2 remains unchanged in healthy target and diabetic patients. The initial data exploratory has shown that sensor 4 capturing on centerntration of hydrogen can be a good predictor for classifiying diabetic patients through urine odour. D. Implementating Experiments Two experiment were designed in this study. Both experiment 1 and 2 consist of 40 urine data samples each from diabetic patients and healthy subjects. Different signal preprocessing approaches were employed for experiment 1 and experiment 2, hence inducing two different ANFIS models. In experiment 1, the average signal values of all training samples from the same subject were calculated according to each sensor type before feeding into the learning model. Experiment 2 treated each data sample as a single data stream. The ANFIS
100 0
0
100
200
300
400
500 600 Time(s)
700
800
900
1000
Fig. 4 Sensor Response on Urine Samples from Healthy Subjects
E. Constructing ANFIS Model In this paper Adaptive Neural Fuzzy Inference System classifier is used to classify the data sample into two classes, i.e. the diabetic patient and the healthy subject. ANFIS [12] mergers the mechanism of fuzzy inference system in architecture neural networks. The fuzzy inference system used is the model of Takagi-Sugeno-Kang (TSK) first order with consideration on simplicity and ease of computation. The ANFIS structure [13] of four inputs and one output were constructed in this study as shown in Fig. 5. The proposed ANFIS model has five layers serving different purposes.
Layers 1. Nodes in this layer are adaptive node, where its parameters can be changed according to node function in Equation (1) to (4). 𝜊1,𝑖 = 𝜇𝐴𝑖 (𝑆1), 𝑖 = 1,2,3,4,5,6
(1)
𝜊1,𝑖 = 𝜇𝐵𝑖−2 (𝑆2), 𝑖 = 1,2,3,4,5,6
(2)
𝜊1,𝑖 = 𝜇𝐶𝑖−3 (𝑆3), 𝑖 = 1,2,3,4,5,6
(3)
𝜊1,𝑖 = 𝜇𝐷𝑖−4 (𝑆4), 𝑖 = 1,2,3,4,5,6
(4)
Layers 5. In this layer, there is only one fixed node to add up all the inputs, as shown in Equation (8). 𝑂5,𝑖 = ∑𝑖 ̅̅̅𝑓 𝑤𝑖 𝑖 =
∑𝑖 𝑤𝑖 𝑓𝑖 ∑𝑖 𝑤𝑖
𝑖 = 1,2,3,4,5,6
(8)
The proposed ANFIS model was constructed in MATLAB environment. Gaussian membership function was used in the fuzzy inference system. Six fuzzy rules were set up for rules evaluation as shown in Fig. 6.
where S1 to S4 represent Sensor 1 to Sensor 4 respectively; 𝐴𝑖 , 𝐵𝑖 , 𝐶𝑖 , 𝐷𝑖 𝑖 = 1, 2, … 6 are linguistic labels associated to the node function.
Fig. 6 Rule Viewer for ANFIS prediction model
Fig. 5 ANFIS architecture developed in this paper
IV. RESULT AND DISCUSSION
Layers 2. Nodes in this layer are fixed parameters aim to multiply each signal input received by the node as in Equation (5). 𝑂2,𝑖 = 𝑤𝑖 = 𝜇𝐴𝑖 (𝑆1) ∗ 𝜇𝐵𝑖 (𝑆2) ∗ 𝜇𝐶𝑖 (𝑆3) ∗ 𝜇𝐷𝑖 (𝑆4), 𝑖 = 1,2,3,4,5,6 (5) Layer 3. Each node in this layer aims to normalized the degree of firing strength as in Equation (6). 𝑂3,𝑖 = 𝑤 ̅=
𝑤𝑖 𝑤1 +𝑤2
, 𝑖 = 1,2,3,4,5,6
TABLE II.
THE PERFORMANCE OF ANFIS MODEL PREDICTION
Experiment 1 Data Validation Accuracy % Precision% Recall%
50-50
60-40
100 100 100
100 100 100
(6)
Layers 4. Each node in this layer is an adaptive node to node function as in Equation 7. 𝑂4,𝑖 = ̅̅̅𝑓 𝑤𝑖 𝑖 = ̅̅̅(𝑝 𝑤𝑖 𝑖 (𝑆1) + 𝑞𝑖 (𝑆2) + 𝑟𝑖 (𝑆3) + 𝑠𝑖 (𝑆4)), 𝑖 = 1,2,3,4,5,6 (7) Where {𝑝, 𝑞, 𝑟, 𝑠, } are consequent adaptive parameter.
The confusion matrix of the ANFIS model performance from both experiments are shown in Table II.
70-30
80-20
90-10
100 100 100
93.75 86.67 92.86
94.44 94.12 94.12
Experiment 2 Data Validation Accuracy% Precision% Recall%
50-50
60-40
70-30
80-20
90-10
64.63 71.66 72
64.27 69.66 71.79
64.88 67.57 70.12
62.41 69.91 70.73
63.96 69.57 71.95
It can be observed that the ANFIS model performed better (in experiment 1) when it is trained using the average signal values of all training samples from the same subject for each sensor type. ANFIS was able to take the benefits of fuzzy logic
implementation, which exploits the tolerance for imprecision based on knowledge–driven reasoning and neural network which learns to do tasks by considering examples based on data– driven approximation. Furthermore the non-fasting urine samples provides better learning and adaptive capabilities for the proposed ANFIS model to improve the detection process of diabetic patients. These results, which achieved 96% average accuracy in discriminating diabetic patient from healthy subject is essential towards the flexibility of diabetic dianogstic test where it does not require patient to fast.
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V. CONCLUSION In this paper, we have investigated the performance of ANFIS model in handling urine odour analysis for diabetes detection. Results obtained from the experiments have shown that both fasting and non-fasting urine samples are able to be used for diabetes prediction. By averaging the data samples according to sensors increase the detection performance because it produce a generalized threshold. However, data streams without averaging process will generate richer information for odour analysis especially when there is noise involved. The performance of the proposed ANFIS model achieved at least 64% using data streams while it has recorded more than 93% correct detection using the averaging process. In conclusion, diabetes prediction through acetone level is encouraging and should be utilized and acceptable in more clinical test. In conclusion, the findings of this study has critically improved the diabetes detection process where diabetes dianogstic test can be performed at any time not limited to fasting requirement. The next step of this study should investigate on the suitable odour sensors to increase the robustness of the proposed model.
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ACKNOWLEDGMENT This work is funded by Universiti Teknikal Malaysia Melaka (UTeM) through the short term research grant, PJP/2015/FTMK(5B)/S01438. We would also like to thank CITO laboratory, Semarang Central Java, Indonesia for their contribution and participation in data collection.
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