Iterative Dichotomizer 3 (ID3) Decision Tree: A Machine Learning Algorithm for Data Classification and Predictive Analysis

Ogheneovo, Edward E.; Nlerum, Promise A.

doi:10.22161/ijaers.74.60

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…Whereas Han and his co-authors presented a machine-learning algorithm SVM which was rulebased extraction of features, Random Forest for prediction of diabetes. The proposed system gives an accuracy of 94.2% [15]. In the same way, Shetty and Joshi also gave a tool for diabetes prediction using data mining techniques.…”

Section: Literature Reviewmentioning

confidence: 71%

Machine Learning-based Web Application for Early Diagnosis of Diabetes

Shahwani

2020

Journal of Applied and Emerging Sciences

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Diabetes has become a chronic disease that seriously threatens human health. It is a group of metabolic diseases characterized by hyperglycemia and there is no role of the age factor involved. The long-term of diabetes disease causes chronic damage and dysfunction of various tissues, especially the eyes, kidneys, heart, blood vessels, and nerves. Most of the time people are not sure about this common disease at the early stage and unluckily the patient moves to a critical situation to meet with major disease due to the continuous effect of diabetes. This research is conducted to build the machine learning-based web application platform for the early diagnosis of the disease, freely accessible anywhere anytime. We used the benchmark dataset named PIDD (Prima Indian Diabetes Dataset) and performed the comparative analysis among the Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest and Support Vector Machines. Based on the classification performance, we found that SVM performed the best among the pool of mentioned algorithms and, therefore, adopted for the development of the intelligent web application for the diabetes diagnosis.

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Section: Literature Reviewmentioning

confidence: 71%

Machine Learning-based Web Application for Early Diagnosis of Diabetes

Shahwani

2020

Journal of Applied and Emerging Sciences

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“…According to the TRIPOD statement 46 , this internal validation approach was preferred to the more classical sample-split approach due to its better reliability in reducing the bias and the variability of performance estimates. Iterative Dichotomiser 3 (ID3) algorithm 47 , 48 was applied to cluster risk classes of clinical relevance.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a scoring system for pre-surgical and early post-surgical prediction of bariatric surgery unsuccess at 2 years

Bioletto

Pellegrini

D’Eusebio

et al. 2021

Sci Rep

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Bariatric surgery (BS) is an effective treatment for morbid obesity. However, a simple and easy-to-use tool for the prediction of BS unsuccess is still lacking. Baseline and follow-up data from 300 consecutive patients who underwent BS were retrospectively collected. Supervised regression and machine-learning techniques were used for model development, in which BS unsuccess at 2 years was defined as a percentage of excess-weight-loss (%EWL) < 50%. Model performances were also assessed considering the percentage of total-weight-loss (%TWL) as the reference parameter. Two scoring systems (NAG-score and ENAG-score) were developed. NAG-score, comprising only pre-surgical data, was structured on a 4.5-point-scale (2 points for neck circumference ≥ 44 cm, 1.5 for age ≥ 50 years, and 1 for fasting glucose ≥ 118 mg/dL). ENAG-score, including also early post-operative data, was structured on a 7-point-scale (3 points for %EWL at 6 months ≤ 45%, 1.5 for neck circumference ≥ 44 cm, 1 for age ≥ 50 years, and 1.5 for fasting glucose ≥ 118 mg/dL). A 3-class-clustering was proposed for clinical application. In conclusion, our study proposed two scoring systems for pre-surgical and early post-surgical prediction of 2-year BS weight-loss, which may be useful to guide the pre-operative assessment, the appropriate balance of patients’ expectations, and the post-operative care.

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“…For the normalization of the features, two main approaches are commonly used: Z-score [ 29 ] and Unity Standard Deviation (SD) [ 30 ]. Finally, in the classification stage, different models are utilized such as: Multilayer Perceptron Neural Network (MLPNN) [ 31 ], Quantum Neural Network (QNN) [ 32 ], Radial Basis Function Neural Network (RBFNN) [ 33 ], Fuzzy C-Means Clustering (FCM) [ 34 ], ID3 Decision Tree [ 35 ], Support Vector Machine (SVM) [ 36 ], Type2 Fuzzy Clustering Neural Network (T2FCNN) [ 37 ], and Probabilistic Neural Network (PNN) [ 38 ].…”

Section: Background and Related Workmentioning

confidence: 99%

ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring

Ismail

Jovanovic

Ramzan

et al. 2023

Sensors

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Increased life expectancy in most countries is a result of continuous improvements at all levels, starting from medicine and public health services, environmental and personal hygiene to the use of the most advanced technologies by healthcare providers. Despite these significant improvements, especially at the technological level in the last few decades, the overall access to healthcare services and medical facilities worldwide is not equally distributed. Indeed, the end beneficiary of these most advanced healthcare services and technologies on a daily basis are mostly residents of big cities, whereas the residents of rural areas, even in developed countries, have major difficulties accessing even basic medical services. This may lead to huge deficiencies in timely medical advice and assistance and may even cause death in some cases. Remote healthcare is considered a serious candidate for facilitating access to health services for all; thus, by using the most advanced technologies, providing at the same time high quality diagnosis and ease of implementation and use. ECG analysis and related cardiac diagnosis techniques are the basic healthcare methods providing rapid insights in potential health issues through simple visualization and interpretation by clinicians or by automatic detection of potential cardiac anomalies. In this paper, we propose a novel machine learning (ML) architecture for the ECG classification regarding five heart diseases based on temporal convolution networks (TCN). The proposed design, which implements a dilated causal one-dimensional convolution on the input heartbeat signals, seems to be outperforming all existing ML methods with an accuracy of 96.12% and an F1 score of 84.13%, using a reduced number of parameters (10.2 K). Such results make the proposed TCN architecture a good candidate for low power consumption hardware platforms, and thus its potential use in low cost embedded devices for remote health monitoring.

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Iterative Dichotomizer 3 (ID3) Decision Tree: A Machine Learning Algorithm for Data Classification and Predictive Analysis

Cited by 10 publications

References 11 publications

Machine Learning-based Web Application for Early Diagnosis of Diabetes

Machine Learning-based Web Application for Early Diagnosis of Diabetes

Development and validation of a scoring system for pre-surgical and early post-surgical prediction of bariatric surgery unsuccess at 2 years

ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring

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