Classification of diabetes disease using decision tree algorithm (C4.5)

Zheng

Frontiers in Artificial Intelligence and Applications

et al. 2022

In order to explore the classification methods from imaging data, we propose to establish the feature classification order by building a classification tree with characteristic factors of brain functional network aided by calculating of network topology based on graph theory. By preprocessing the imaging data and modeling the networks with professional platform, we get the topology parameters as the input of the C4.5 decision tree, and then get the classification feature and classification tree by calculating the information gain ratio. The results show that the classification prefers holistic characteristics and quality characteristics. By programming and the samples test, it is feasible to extend the classification with network characters learning based on the processing results of multi-modal imaging data and machine learning algorithms.

Section: Discussionmentioning

confidence: 99%

“…Permana et al have discussed the influential variable in so many diabetes variables by C4.5 decision tree algorithm [16]. Aim to test the effect of the indexes, in this paper we use the C4.5 decision tree to verify the classification in the imaging data.…”

Section: Introductionmentioning

confidence: 99%

Classification of Characteristic Factors of Brain Functional Network Using Decision Tree

Zheng

Frontiers in Artificial Intelligence and Applications

et al. 2022

“…And so on until the leaves are formed. The C4.5 algorithm can produce a decision tree, where the square symbol represents the node and the ellipse represents the leaf (Permana, et al, 2021). 2.…”

Section: Methodsmentioning

confidence: 99%

Implementation of The C4.5 Algorithm in Describing The Trends of The Human Consciousness and Unconsciousness

Syahputra

Putra

2022

JAETS

The human mind has two properties that have different and conflicting functions. The two characteristics of the mind are the conscious mind and the subconscious mind. This study uses the C4.5 data mining algorithm to describe or see the tendency of the conscious and subconscious or the level of suggestion. Suggestibility is the most important thing in hypnotherapy. Hypnotherapy is a therapy performed under hypnosis. Hypnosis is communication with the human subconscious. The C4.5 algorithm for Data Mining is used to form a decision tree. This research will produce a decision tree that can explain the suggestive level of a series of tests that have been carried out. Testing is done with RapidMiner software to get a decision tree. The test consists of a series of tests consisting of four types of tests, where test 1 is to measuring right brain dominance , test 2 is to measure the speed of receiving instructions, test 3 is to measure a person's creativity, and test 4 is to know person's level of understanding, reasoning and imagination. The results of manual calculations were carried out in this study later with the results obtained from the results of testing with the RapidMiner software

Int J Imaging Syst Tech

“…The fuzzy rule base produces IF-Then rules by applying the C4.5 decision tree algorithm. [36][37][38] And in later cases, the fuzzy rule is converted into the crisp function to get a fuzzy base. Medical experts estimate the fuzzy rules and delete them and add them to the fuzzy base for fuzzy rule verification.…”

Section: Architecture Of Ifir_pddmmentioning

confidence: 99%

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

Nagaraj

Deepalakshmi

2022

Diabetes is one of the most common and hazardous diseases, which can affect almost every organ in the body. Diagnosis of diabetes requires determining all vital parameters related to the disease. However, the nature of the data from those parameters is very uncertain, affecting the process of disease diagnosis. This article proposes an intelligent fuzzy inference rule-based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes. The suggested model employs an inference technique that medical specialists have validated for recommendations. IFIR_PDDM comprises three elements used to forecast the risk of diabetes disease. Initially, a fuzzy membership function utilizes medical recommendations and statistical methodologies. Medical specialists then validate the mining-based rules using a decision tree rule induction technique. The proposed model predicts the risk of diabetes disease using fuzzy inference based on Mamdani's technique. Based on this information, the recommendations for a normal life, nutrition, exercise, and medications are given to patients. We used an electronic health record (EHR) medical and clinical dataset from the PIMA Indian Diabetes dataset to develop our proposed model and assess its performance. The proposed model takes less time for diabetes diagnosis, and the expert recommendation system uses the fuzzy inference method.