fuzzy clustering

One of the currently important and widely used research subjects in the healthcare area of cancer patients is the diagnosis procedure of cancer tumors and metastases in magnetic resonance imaging such that it has a high level of accuracy and also be a support for doctors in interpreting and diagnosing medical data. To this aim, a multivariate Hotelling’s T2 control chart is used.

Using a two-dimensional discrete wavelet transform, some features of the image texture are extracted by using statistical and transform methods. Then, to reduce the data dimensions and feature selection, a genetic algorithm is used. Afterward, two methods including fuzzy c-Means clustering algorithm and a multivariate Hotelling’s T2 control chart are used to diagnose bone marrow metastasis patients.

From 204 bone marrow samples, 76 features are extracted from which six ones are selected and a 204×6 feature vector matrix is generated. Finally, the performance of the proposed two methods is compared. The results show that the diagnosis and accuracy measures of multivariate Hotelling’s T2 control chart are better than the other method.

In the context of cancer, one of the current concerns for healthcare providers is to use non-invasive, short response time, and highly accurate methods in diagnosing tumors and metastases. The proposed method appropriately addresses these requirements.

Nowadays, medical sciences and physicians face a huge amount of data. Diabetes is one of the most expensive glands in the world. Since it is not always easy to diagnose the disease, the physician should examine the outcome of patient tests and decisions made in the past for patients with similar conditions to make an appropriate decision. Due to the large number of patients and the multiple tests performed on each patient, an automated tool for exploring previous patients is needed.

One of the most important methods used to derive data is data mining. Due to the high number of diabetic patients, timely diagnosis and treatment of this disease can reduce the risk of death and its associated medical costs. So far, different systems have been proposed for the diagnosis and prediction of diabetes, but fuzzy logic based systems are used in this study to increase accuracy and efficiency. In the proposed model, fuzzy clustering is first grouped into separate clusters, and then the radial neural network is predicted for each patient with diabetes mellitus. A compatible neuro-fuzzy inference system has also been used to diagnose diabetes.

In this paper different classification techniques have been used in MATLAB software to diagnose diabetes mellitus and to classify patients as diabetic and non diabetic. The dataset used is extracted from the UCI database. The accuracy of the proposed method is 97.14% which is significantly higher than other models of diabetes diagnosis.

The application of two fuzzy models has significantly improved the accuracy of diagnosis of diabetes compared to other models proposed in this field.

  Empathy is a skill that has been proved effective in learning and teaching processes. The aim of this study was to explore the relationship between students’ empathy and their learning behaviors. A fuzzy clustering-based method (an area of artificial intelligence) was used, according to which students were classified to clusters based on their empathy measures. Students’ empathy was assessed through a questionnaire. Overall, 345 students (11 to 13 years old) from six schools located in three different areas of Tehran, Iran, participated in this study, selected by multistage cluster sampling. In this method, similar samples are classified in one cluster and, then, clusters can be labeled based on their attributes (empathy measures). Two teacher-reported and student-reported questionnaires were used to assess the learning behavior and empathy levels of students. Questionnaires were completed by the students and their teachers during school year 2017 and 2018 (from autumn 2017 to spring 2018). All calculations were performed in MATLAB, a multi-purpose programming environment. Although statistical parameters showed a strong relationship between students’ empathy and their learning behaviors, AI clustering process provides a more exact analysis due to its nature. The results revealed a significant relationship between empathy scores and learning among male students. A P value of 0.0031 indicates a meaningful relationship between empathy scores and learning behavior measures. Number of students in each cluster showed that females are more uniform than males in the sense of empathy. Cultural backgrounds have significant effects on answers to questions. Processes revealed a meaningful difference between males and females when their connection of empathy and learning behaviors were investigated. Cognitive components seem to be more determinative than affective components