Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests

A Predictive model for liver disease progression based on logistic regression algorithm

Liver disease counts to be one of the most prevalent diseases in the worldwide. Therefore, this paper is aim to address the problem of predicting liver disease progression. As the existing predictive models focus on predicting the label of disease; the probability of developing the disease is still obscure. This paper, therefore, has proposed a model to predict the probability occurrence of liver diseases. The proposed predictive model used logistic regression abilities to predict the probability of liver disease occurrence. ILPD dataset was used to analyze the performance of the model. The predictive model has shown outstanding performance with a prediction accuracy rate of 72.4%, the sensitivity of 90.3%, the specificity of 78.3 %, Type I Error of 9.7 %, Type II Error of 21.7 %, and ROC of 0.758%. The model has furthermore confirmed the feasibility of the laboratory tests such as as (Age; Direct Bilirubin (DB), Alamine_Aminotransferase (SGPT), Total_Protiens (TP), Albumin (ALB)) to predict the disease progression. The predictive model will be helpful to patients and doctors to realize the progression of the disease and make a suitable timely intervention

Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

As machine learning models are increasingly applied to real-world scenarios, it is essential to consider the possibility of changes in the data distribution over time. Concept drift detection and adaptation refers to the process of identifying and tracking these changes and updating the model accordingly. Researchers have devoted significant efforts to develop various techniques and tools for concept drift detection and adaptation, as this paper provides a generic roadmap and review of the field. In this paper, we begin by reviewing the background of data stream classification and its assumptions and requirements. Then, we explore the historical development of concept drift detection and adaptation and highlight the key points of approaches that have emerged over time. Next, we summarize the major findings, challenges, and limitations of past research, and provide insights into potential future directions of the field. The paper can benefit researchers and practitioners who seek to navigate the challenges and opportunities in concept drift detection and adaptation

Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

As machine learning models are increasingly applied to real-world scenarios, it is essential to consider the possibility of changes in the data distribution over time. Concept drift detection and adaptation refers to the process of identifying and tracking these changes and updating the model accordingly. Researchers have devoted significant efforts to develop various techniques and tools for concept drift detection and adaptation, as this paper provides a generic roadmap and review of the field. In this paper, we begin by reviewing the background of data stream classification and its assumptions and requirements. Then, we explore the historical development of concept drift detection and adaptation and highlight the key points of approaches that have emerged over time. Next, we summarize the major findings, challenges, and limitations of past research, and provide insights into potential future directions of the field. The paper can benefit researchers and practitioners who seek to navigate the challenges and opportunities in concept drift detection and adaptation