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1. MOHD IZZAT AMRIE BIN SUHAINI - 80136

2. KAMIL HAKIM BIN KAMARUDDIN - 79732

3. MUHAMMAD HAKIMI BIN YA'RAHIM - 80238

4. NURSE BINTI JIKOL - 80796

5. PRESSHIELLA L'SE ANAK HILLARY - 80981

6. MOHD ZAKHRI BIN MOHAMAD ZAINAL - 80152

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The fuzzy logic system we propose is a Fuzzy Logic Medical Diagnosis System, which is specifically developed to diagnose diabetes. This technique seeks to assess a patient's diabetes risk using a variety of health parameters.

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The context and variables of the proposed problem

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The context and variables of the proposed problem

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The proposed problem involves the development of a Fuzzy Logic Medical Diagnosis System to diagnose diabetes by changing a patient's diabetes risk based on health parameters such as fasting blood glucose levels, postprandial blood glucose levels, body mass index (BMI), and age. This system uses fuzzy logic to provide a more accurate and realistic risk assessment than traditional binary logic, which only provides an absolute value of 0 or 1. By assessing the risk of diabetes at various levels of membership, this system can better deal with uncertainty and variation in medical data. Independent variables include fasting blood glucose levels, postprandial blood glucose levels, BMI, and age, while the dependent variable is diabetes risk, measured in percentages from 0% to 100%. The system aims to help healthcare providers make more accurate and informed decisions, improving the quality of diagnosis and care given to diabetes patients.

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Hierarchical Fuzzy Model for Diabetes Risk Evaluation 

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Hierarchical Fuzzy Model for Diabetes Risk Evaluation 

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Input: Postprandial Blood Glucose Level, Post-Mealtime Range

Output: Glucose Risk

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Input: Fuzzy sets of the linguistic variable (Postprandial Blood Glucose Level, mg/dL)

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Input: Fuzzy sets of the linguistic variable (Postprandial Blood Glucose Level, mg/dL)

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Input: Fuzzy sets of the linguistic variable (Post-Mealtime Range, hours)

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Input: Fuzzy sets of the linguistic variable (Post-Mealtime Range, hours)

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Output: Fuzzy sets of the linguistic variable (Glucose Risk, mg/dL)

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Output: Fuzzy sets of the linguistic variable (Glucose Risk, mg/dL)

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Input: Body Mass Index (BMI), Age

Output: Health Risk

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Input: Fuzzy sets of the linguistic variable (Body Mass Index, Range: 15-40) 

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Input: Fuzzy sets of the linguistic variable (Body Mass Index, Range: 15-40) 

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Input: Fuzzy sets of the linguistic variable (Age, Range: 20-80) 

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Input: Fuzzy sets of the linguistic variable (Age, Range: 20-80) 

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Output: Fuzzy sets of the linguistic variable (Health Risk, %) 

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Output: Fuzzy sets of the linguistic variable (Health Risk, %) 

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Input: Glucose Risk, Health Risk

Output: Diabetes Risk

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Input: Fuzzy sets of the linguistic variable (Glucose Risk, mg/dL)

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Input: Fuzzy sets of the linguistic variable (Glucose Risk, mg/dL)

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Input: Fuzzy sets of the linguistic variable (Health Risk, %) 

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Input: Fuzzy sets of the linguistic variable (Health Risk, %) 

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Output: Fuzzy sets of the linguistic variable (Diabetes Risk, %) 

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Output: Fuzzy sets of the linguistic variable (Diabetes Risk, %) 

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Fuzzy Rules for Rule Base 1: Glucose Risk 

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Fuzzy Rules for Rule Base 1: Glucose Risk 

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Fuzzy rules for Rule Base 2: Health Risk 

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Fuzzy rules for Rule Base 2: Health Risk 

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 Fuzzy rules for Rule Base 3: Diabetes Risk 

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 Fuzzy rules for Rule Base 3: Diabetes Risk 

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A fuzzy expert system to solve the proposed problem

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A fuzzy expert system to solve the proposed problem

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A three-dimensional plot for Rule Base 1 

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A three-dimensional plot for Rule Base 1 

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A three-dimensional plot for Rule Base 2 

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A three-dimensional plot for Rule Base 2 

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A three-dimensional plot for Rule Base 3 

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A three-dimensional plot for Rule Base 3 

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Alex, a 30-year-old man who is passionate about sports and enjoys playing basketball several times a week, has recently been experiencing fluctuations in his energy levels and occasional dizziness. Concerned about these symptoms, he decides to undergo a diabetes risk assessment. His postprandial blood glucose level is 140 mg/dL (moderate), the post-mealtime range is 1 hour, his Body Mass Index (BMI) is 24 (normal), and his age is 30 years. Alex is eager to understand his glucose risk, health risk, and overall diabetes risk based on these inputs.

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Case Study 2: Person Who Likes to Eat Sweet Food 

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Case Study 2: Person Who Likes to Eat Sweet Food 

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Sarah, a 45-year-old woman with a sweet tooth, enjoys indulging in desserts and sweet treats regularly. Despite her love for sweet food, she's concerned about the potential health risks associated with her diet and wants to be proactive in managing her health, especially her risk of diabetes. Her postprandial blood glucose level is 180 mg/dL (high), her post-mealtime range is 2 hours, her Body Mass Index (BMI) is 28 (overweight), and her age is 45 years. Sarah is keen to understand her glucose risk, health risk, and overall diabetes risk based on these inputs.

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Modified Fuzzy Inference System (FIS) for edge detection

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Modified Fuzzy Inference System (FIS) for edge detection

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The axes serves as the location for loading related images. There are five buttons with functions such as 'Load Image', 'Convert to Grayscale', 'Detect Edges', 'Show Gradients', 'Edit Rules' and 'Plot MF Functions'.

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Now, users can select an image using a load image, and the selected image will be displayed in the GUI's.

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New Fuzzy Inference System (FIS) for edge detection

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New Fuzzy Inference System (FIS) for edge detection

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An edge detecting procedure is done on the previously equalized picture, which is saved in the variable equalizedImage.

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New membership functions of the inputs and outputs of edge FIS

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New membership functions of the inputs and outputs of edge FIS

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Input: Fuzzy sets of the linguistic variable (lx) 

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Input: Fuzzy sets of the linguistic variable (lx) 

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Input: Fuzzy sets of the linguistic variable (ly) 

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Input: Fuzzy sets of the linguistic variable (ly) 

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Output: Fuzzy sets of the linguistic variable (lout) 

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Output: Fuzzy sets of the linguistic variable (lout) 

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The outcome of this project is the development and implementation of intelligent expert systems and fuzzy image processing using MATLAB software. Its goal is to provide an overview of the theoretical foundations of fuzzy logic, discuss practical methods of building fuzzy expert systems, and present various techniques for fuzzy image processing.The presentation of current research on the application and advancements in these areas will be a significant part of this study, as it explores the potential of fuzzy logic in computer intelligence and image analysis.This project highlights the importance of fuzzy expert systems and fuzzy image processing in the context of MATLAB and shows their relevance to current research and development in a constantly changing world.

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80796 - Our fuzzy expert system for diabetes risk assessment, developed using MATLAB, represents an important step forward in medical diagnostics. By considering various health parameters like postprandial blood glucose, post-mealtime range, BMI, and age, the system provides a detailed evaluation of diabetes risk. Fuzzy logic helps manage the uncertainties in medical data, offering more reliable results than traditional methods. The system uses three rule bases to assess glucose risk, health risk, and overall diabetes risk, ensuring a thorough analysis. This approach helps healthcare professionals make better-informed decisions, ultimately improving patient care. Additionally, the user-friendly GUI makes the system easy to use in clinical settings. This project highlights the potential of fuzzy logic in solving complex real-world problems in healthcare.

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• What are the problems or limitations faced in the project?

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• What are the problems or limitations faced in the project?

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80136 - Some limitations are cause by device where the Matlab always crash, and it does require a lot of disk space. Another limitation is variable, that use for this case study is quite broad since diabetes, is not only cause by food. It is also causes by genetic. Other problem is, im having a hard time to create the fis file, and also kind struggling to make the GUI since i need to split the based code on the .m file.

80796 - One of the significant limitations faced in our project was the handling of uncertainties in medical data. While fuzzy logic helps address this issue to some extent, it does not completely eliminate the inherent ambiguity in the data. This challenge is particularly evident in the input variables such as postprandial blood glucose levels, post-mealtime range, BMI, and age. These parameters can vary widely among patients, leading to potential inaccuracies in the risk assessment. Moreover, the complexity of creating and fine-tuning the fuzzy rule bases to ensure accurate results also posed a significant hurdle. Another limitation was related to the implementation in MATLAB, where the customization and integration of various toolboxes required a deep understanding of the platform and sometimes led to technical difficulties.