An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease

Haruna, Ahmed Abba; Muhammad, L. J.; Yahaya, Basira; Garba, E. J.; Oye, N. D.; Jung, Low Tan

doi:10.1109/icd47981.2019.9105844

Cited by 23 publications

(16 citation statements)

References 11 publications

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“…The ML algorithms ara able to read and modify its structure based on a set of observed data with adaptation done by optimizing over a cost function or an objective [14,39]. ML has already shown potential for diagnosing, detecting, containing, and therapeutic motoring of many diseases [10][11][12]. ML techniques can be classified in four ways:…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

et al. 2020

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COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.

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Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

et al. 2020

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“…For many years, ML has solved many sophisticated and complex real world problems in the application areas such as marketing, business and retails applications, natural language processing, health care, autonomous vehicle system, intelligent robots, climate change, image processing, voice, gaming, among others. ML techniques had been used for the prediction and diagnosis of many diseases such as COVID-19 pandemic, malaria, typhoid, coronary artery diseases, diabetes mellitus, among others [8,9]. ML algorithms are typically based on the trial and error approach which is quite opposite to conventional algorithms that follow the programming instruction based on like if-else decision statements [10].…”

Section: Introductionmentioning

confidence: 99%

Predictive Supervised Machine Learning Models for Diabetes Mellitus

2020

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Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively. Keywords Machine learning • Predictive model • Diabetes mellitus • Diabetes mellitus type 2 • Random forest This article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar".

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“…In Nigeria, CAD causes more deaths due to insufficient knowledge of the negative impact of the disease on humans [5] [23,24]. In Nigeria, CAD-related deaths reached 53,836 which amount to 2.82% of all deaths that occurred in 2014 [20] [24]. Most of the victims of the disease tend to ignore the early symptoms and consultations with the health workers until they are in a bad or severe condition of the CAD.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, various uncertainties and vagueness affect the diagnostic process, and they must be carefully dealt with [7]. Sometime also, physicians often have variations in their decisions due to uncertainty and vagueness of the information they have at their disposal [18,24,25]. These uncertainties, complexities and vagueness involved in the diagnostic decision-making process have to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy based expert system for diagnosis of coronary artery disease in nigeria

Muhammad

Algehyne

2021

Health Technol.

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Expert system is an artificial intelligence based system that imitates the decision making ability of human and it is used as the diagnostic tool for many diseases including diabetes mellitus, COVID-19, cancers, coronary artery disease (CAD), among other diseases. Even though CAD is globally one of the deadliest diseases and it is not well known in Nigeria, it causes many deaths as such in 2014, 53,836 or 2.82% of total deaths in Nigeria resulted from the CAD. In this study, fuzzy based expert system for diagnosis of CAD is developed in order to provide the complementary diagnostic tools for diagnosis of CAD’s patients in Nigeria. The improved C4.5 data mining algorithm is used to transfer the knowledge of human expert to the knowledge base on the expert system instead of using conventional techniques such as interviews, questionnaires, etc. Taken together, the performance evaluation system was carried out, and the system has an overall accuracy, sensitivity and specificity of 94.55%, 95.35% and 95.00% respectively; which show that, the system is reliable and capable of diagnosing both negative and positive cases of CAD patients efficiently.

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An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease

Cited by 23 publications

References 11 publications

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

Predictive Supervised Machine Learning Models for Diabetes Mellitus

Fuzzy based expert system for diagnosis of coronary artery disease in nigeria

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