Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers

Nasser, Fatin Kadhim; Behadili, Suhad Faisal

doi:10.24996/ijs.2022.63.11.34

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Nasser and Behadili [88] conducted a comparative study of DT and KNN algorithms for BC detection. The DT with feature selection achieved 87.83% accuracy.…”

Section: Sathiyanarayanan Et Almentioning

confidence: 99%

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Alsabry,

Algabri,

Ahsan

2023

jast

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Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.

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“…Nasser and Behadili [88] conducted a comparative study of DT and KNN algorithms for BC detection. The DT with feature selection achieved 87.83% accuracy.…”

Section: Sathiyanarayanan Et Almentioning

confidence: 99%

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Alsabry,

Algabri,

Ahsan

2023

jast

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“…A decision tree is divided into two parts: leaf nodes that are labeled, and internal nodes that do not have child nodes that help make decisions. Decision trees accommodate various data formats in categorizing occurrences [27]. The decision tree steps are shown in the algorithm (2).…”

Section: Decision Treementioning

confidence: 99%

Machine Learning Prediction of Brain Stroke at an Early Stage

Kadhim,

Radhi

2023

Iraqi Journal of Science

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The healthcare sector has traditionally been an early adopter of technological progress, gaining significant advantages, particularly in machine learning applications such as disease prediction. One of the most important diseases is stroke. Early detection of a brain stroke is exceptionally critical to saving human lives. A brain stroke is a condition that happens when the blood flow to the brain is disturbed or reduced, leading brain cells to die and resulting in impairment or death. Furthermore, the World Health Organization (WHO) classifies brain stroke as the world's second-deadliest disease. Brain stroke is still an essential factor in the healthcare sector. Controlling the risk of a brain stroke is important for the survival of patients. In this context, machine learning is used in various health-related fields, especially "brain stroke." To that end, an automated model for recognizing and providing helpful information for brain stroke prediction was created. It can predict brain strokes with high accuracy in the early stages. The proposed model aims to examine the patient for effective decision-making. This research study employed a freely accessible dataset and a mix of machine learning methods such as random forest, logistic regression, and decision trees. Furthermore, the Synthetic Minority Over Sampling Technique (SMOTE) was implemented to handle unbalanced data. The result shows a high accuracy of 99% in predicting a brain stroke.

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“…Deposition time was 4 hours for all samples. More details on the optimum preparation conditions can be found elsewhere [35][36][37][38][39][40][41].…”

Section: Experimental Workmentioning

confidence: 99%

“…Over many decades, Si 3 N 4 powders and thin films with different phases have been prepared using different methods and techniques, including chemical and physical, such as ion-assisted deposition [16,17], heating powdered silicon [18,19], carbothermal reduction [20][21][22], chemical vapor deposition (CVD) [23][24][25][26][27], plasma-enhanced CVD [28], nitrogen glow discharge [29,30], atomic layer deposition (ALD) [31][32][33], silaneammonia reaction [34], and reactive sputtering [35][36][37][38][39][40][41][42][43]. However, the effect of the electrical conductivity of silicon used as a precursor of silicon nitride was not determined.…”

Section: Introductionmentioning

confidence: 99%

Employment of Silicon Nitride Films Prepared by DC Reactive Sputtering Technique for Ion Release Applications

Taher,

Hameed

2023

IJP

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In this work, silicon nitride (Si3N4) thin films were deposited on metallic substrates (aluminium and titanium sheets) by the DC reactive sputtering technique using two different silicon targets (n-type and p-type Si wafers) as well as two Ar:N2 gas mixing ratios (50:50 and 70:30). The electrical conductivity of the metallic (aluminium and titanium) substrates was measured before and after the deposition of silicon nitride thin films on both surfaces of the substrates. The results obtained from this work showed that the deposited films, in general, reduced the electrical conductivity of the substrates, and the thin films prepared from n-type silicon targets using a 50:50 mixing ratio and deposited on both surfaces of a titanium substrate reduced the electrical conductivity of this substrate by 30%. This reduction in the release of ions from the coated metal substrate is attributed to the dielectric properties of the deposited silicon nitride thin films. This result is very important and applicable. This work represents the first attempt in Iraq to study such effects and may represent a good starting point for advanced studies in biomedical engineering.

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Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers

Cited by 9 publications

References 21 publications

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Machine Learning Prediction of Brain Stroke at an Early Stage

Employment of Silicon Nitride Films Prepared by DC Reactive Sputtering Technique for Ion Release Applications

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