Blockchain integrated multi-agent system for breast cancer diagnosis

Ramanathan, Thirumalaimuthu Thirumalaiappan; Hossen, J.; Sayeed, Shohel

doi:10.11591/ijeecs.v26.i2.pp998-1008

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…Artificial intelligence (AI) techniques are useful in healthcare applications in providing solutions for diagnosis of diseases through the development of software that can analyze like medical experts [1][2][3][4]. The AI and machine learning techniques are being investigated for the medical diagnosis problem by testing its performance on prediction of unknown cases [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Thirumalaiappan Ramanathan,

Hossen,

Sayeed

2024

IJORAS

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Designing a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.

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Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Thirumalaiappan Ramanathan,

Hossen,

Sayeed

2024

IJORAS

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“…Reddy et al in [15] used dataset from UCI repository to predicte breast cancer by using many algorithms such as Adaboost, multi-layer perceptron and Stacking classifier, a stacking algorithm has been successful in achieving the best accuracy 99.20%; Ramanath et al [16] they suggested a hybrid system based on fuzzy logic approach to supporting breast cancer prediction illness; the suggested system had shown a 96.49% classification accuracy and Wisconsin data was used. Blockchain has been used to provide security for ensuring the fact that only trusted and reputable agents participate participation in the process of the decision-making.…”

Section: Introductionmentioning

confidence: 99%

Comparison of various data mining methods for early diagnosis of human cardiology

Shanshool

Saeed²,

Khaleel³

2023

IJ-AI

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Recent healthcare reports indicate clearly an increasing mortality rates worldwide which puts a significant burden on the healthcare sector due to different diseases. Coronary artery diseases (CAD) is one of the main reasons of these uprising death rates since it affects the heart directly. For early diagnosis and treatment of CADs, a swiftly growing technology called data mining (DM) has been used to collect and categorize necessary data from patients; age, blood sugar and pressure, a type of thorax pain, cholesterol, and so on. Therefore, this paper adopted four DM methods; Decision tree (DT), logistic regression (LR), random forest (RF), and Naïve Bayes (NB) to achieve the goal. The paper utilized the Cleveland dataset along with Python programming language to compare among the four DM methods in terms of precision, accuracy, recall, and area under the curve. The results illustrated that NB method has the best accuracy of 89.47% compared with previous studies which will help with accurate, fast and inexpensive diagnosis of CADs.

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Blockchain integrated multi-agent system for breast cancer diagnosis

Cited by 2 publications

References 42 publications

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Comparison of various data mining methods for early diagnosis of human cardiology

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