F.M. Javed Mehedi Shamrat’s scientific contributions

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Publications (5)


Figure 1. The proposed architecture of the breast cancer detection
Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset
  • Article
  • Full-text available

August 2023

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229 Reads

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7 Citations

Bulletin of Electrical Engineering and Informatics

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F. M. Javed Mehedi Shamrat

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[...]

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According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is being utilized to validate the findings of this study. The comparison was made using the following performance metrics: accuracy, sensitivity, false omission rate, specificity, false discovery rate and area under curve. The LR method achieved a maximum accuracy of 99.12% among all eight algorithms and was compared to other comparable studies in the literature. The five features chosen are used to calculate the model's fidelity-to-interpretability ratio (FIR), which indicates how much interpretability was sacrificed for performance. The uniqueness of this work is the explainability approach taken in the model's performance, which aims to make the model's outputs more understandable and interpretable to healthcare experts.

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Human Face Recognition Using EigenFace, SURF Methods

March 2021

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175 Reads

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7 Citations



Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm

October 2020

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325 Reads

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70 Citations

Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays. Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year. However, patients' lives can be saved with the fast detection of disease in the earliest stage. In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset. In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction. These algorithms are implemented on an online dataset of UCI machine learning repository. The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 99.80% accuracy. Later, different performance evaluation metrics have also been displayed to show appropriate outcomes. To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks.

Citations (4)


... Numerous studies have explored various machine learning algorithms to enhance classification performance. For instance, a maximum accuracy of 99.12% is reported using LR among eight evaluated algorithms [7]. In another study, [39] achieves a peak accuracy of 96.46% using a Neural Network algorithm while assessing 11 different machine learning approaches to classify breast cancer as benign or malignant. ...

Reference:

Clipper: An efficient cluster-based data pruning technique for biomedical data to increase the accuracy of machine learning model prediction
Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset

Bulletin of Electrical Engineering and Informatics

... The dataset consists of 4800 images. We have classified the images[21][22][23][24][25][26] into the following categories.The categories are Bus pictures, Motorbike Pictures, Truck Pictures, Car pictures, etc. The images are in different sizes. ...

Human Face Recognition Using EigenFace, SURF Methods

... in the test information is produced. [19]. The SVM model intends to discover the space in the information lattice where different information gatherings can be generally isolated and draw a hyperplane. ...

Optization of Prediction Method of Chronic Kidney Disease with Machine Learning Algorithm.pdf
  • Citing Conference Paper
  • March 2021

... Furthermore, ML techniques such as support vector machines (SVMs) and ensemble methods have shown significant promise in early CKD detection. Studies like [20][21][22][23] emphasize the role of these methods in improving diagnostic accuracy, enabling timely intervention and better patient outcomes. The integration of these models into clinical practice marks a transformative shift in nephrology care. ...

Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm
  • Citing Conference Paper
  • October 2020