Browsing by Author "Shultana, Shahana"

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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques
(IEEE, 2021-01-22) Ghosh, Pronab; Azam, Sami; Jonkman, Mirjam; Karim, Asif; Shamrat, F. M. Javed Mehedi; Ignatious, Eva; Shultana, Shahana
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).
Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease
(2020 IEEE International Conference for Innovation in Technology (INOCON) , IEEE, 2020-11) Shamrat, F.M. Javed Mehedi; Ghosh, Pronab; Sadek, Mahbubul Hasan; Kazi, Md. Aslam; Shultana, Shahana
The chronic kidney disease is the loss of kidney function. Often time, the symptoms of the disease is not noticeable and a significant amount of lives are lost annually due to the disease. Using machine learning algorithm for medical studies, the disease can be predicted with a high accuracy rate and a very short time. Using four of the supervised classification learning algorithms, i.e., logistic regression, Decision tree, Random Forest and KNN algorithms, the prediction of the disease can be done. In the paper, the performance of the predictions of the algorithms are analyzed using a pre-processed dataset. The performance analysis is done base on the accuracy of the results, prediction time, ROC and AUC Curve and error rate. The comparison of the algorithms will suggest which algorithm is best fit for predicting the chronic kidney disease.
Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm
(2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), IEEE, 2020-11-18) Ghosh, Pronab; Shamrat, F. M. Javed Mehedi; Shultana, Shahana; Afrin, Saima; Anjum, Atqiya Abida; Khan, Aliza Ahmed
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.
Sports Events Classification Using Convolutional Neural Networks
(Daffodil International University, 2018-11) Shultana, Shahana; Moharram, Md. Shakil
Analysis of different sports data to get valuable insight has become immensely important now-a-days. Profuse application of Artificial Intelligence in different sectors has become a very popular trend as well. However, application of AI in sports analytics is still a new research domain left for exploration. With a view to applying AI in sports analytics, we have deployed Inception V3 and MobileNet which are Google's most popular Convolutional Neural Networks to successfully recognize 5 different sports events from a huge image dataset of these events. We also developed a Convolutional Neural Network model which name is SP-Net and we trained our proposed model with these 5 different sports events. SP-Net correctly predicted the class almost all images during the period of testing and gives a high performance. In terms of performance our proposed model SP Net surpass Inception v3 and MobileNet both of these models. Besides, Inception v3 and MobileNet also achieved a very high performance in terms of accuracy, precision, recall and f-measure while applied on the target dataset for successful classification.