Online Diagnostic Expert System for Detection of Breast Cancer in Saudi Arabia

Qawqzeh, Yousuf; Sattar, Khalid Nazim Abdul

doi:10.5120/19833-1686

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Despite significant advances in screening and patient management, BC represents one of the common malignancies in women worldwide and it was ranked as the second most likely cause of cancer mortality. Statistically, there were 268,600 new cases of BC diagnosed in American women in 2019, with 41,760 deaths [1][2][3]. BC is a very diverse illness with numerous forms and subtypes.…”

Section: Introductionmentioning

confidence: 99%

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

Qawqzeh¹,

Alourani²,

Ghwanmeh³

2023

IJACSA

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The goal of this research is to create a machine learning (ML) classifier that can improve breast cancer (BC) diagnosis and prediction. The principle components analysis (PCA) technique is used in this work to minimize the dimensions of the BC dataset and achieve better classification metrics. The developed classifier outperformed others in terms of F1 score and accuracy score. Using the original BC dataset, four different classifiers are applied to determine the best classifier in terms of performance metrics. The used classifiers were RandomForest, DecisionTree, AdaBoost, and GradientBoosting. The RandomForest classifier obtained (95.7%) f1 score and (94.5%) accuracy score, the DecisionTree classifier obtained (93%) f1 score and (91%) accuracy score, the GradientBoosting classifier obtained (95%) f1 score and (93.5%) accuracy score, and the AdaBoost classifier obtained (95.8%) f1 score and (94.5%). The AdaBoost classifier was utilized to create the final model using the reduced PCA dataset because it scored the highest performance metrics. The developed classifier is named as "pcaAdaBoost". The optimized pcaAdaBoost achieved higher performance metrics in terms of f1 score (99%) and accuracy score (98.8%). The results reveal that the optimized pcaAdaBoost scored highest performance measures in terms of crossvalidation and testing outcomes, with an overall accuracy of (99%). The improved results justify the use of dimensionality reduction in high-dimension datasets to reduce complexity and improve performance measures.

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Section: Introductionmentioning

confidence: 99%

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

Qawqzeh¹,

Alourani²,

Ghwanmeh³

2023

IJACSA

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“…In this study, we considered the development of Mobile Neuro-fuzzy System that uses the combination of the intelligence technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose [27][28] [29] and suggest possible treatments for cardiovascular diseases through interactivity with user [30]. Taking the advantage of mobile technology in term of phones that are cheaply available and with the widespread accessibility to provide real time healthcare services.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

Isinkaye¹,

Soyemi²,

Oluwafemi³

2017

IJIEEB

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Abstract-In our present environment, heart diseases are very rampart and they describe the various types of diseases that affect the heart. They account for the leading cause of death word-wide especially, in Africa. It is therefore very important for individuals to have adequate knowledge of their heart health in order to avoid the risk of decreased life expectancy. The high mortality rate of heart (cardiovascular) diseases is attributed to the unequal ratio of patients to scarcity of medical experts who can provide medical care, also patients are not always warned to waiting long hours on queue in the hospital, especially in cases of emergency. This paper designed and implemented a Mobile Neuro-fuzzy System that uses the combination of the intelligent technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for cardiovascular diseases through interactivity with user. It employs programs like MySQL, PHP, JAVA (Android) and XML (Android Studio) while tools like XAMPP, PhpStorm and Android O/S were used to integrate these techniques together. The system proved to be of enormous advantage in diagnosing heart diseases, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.

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Online Diagnostic Expert System for Detection of Breast Cancer in Saudi Arabia

Cited by 2 publications

References 8 publications

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier

A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

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