Credit card fraud detection is a challenging problem that banks and credit card issuers are struggling with and are making huge efforts in implementing fraud detection systems. In today’s banking system, fraud detection is often done by using rule based methods. However, the progress and development of machine learning techniques gives banks and financial institutions the possibility to detect an unusual situation faster for big financial data sets. Machine Learning is a branch of Artificial Intelligence that has become very popular, and useful, in the last 10-15 years. One definition of Machine Learning is that it is the semi-automated extraction of knowledge from data. In this research work discusses different machine learning based credit/debit card fraud detection and challenges.

Classification of arrhythmia patterns is an open field of research. One of the most widely used biological signals crucial for the detection of heart conditions is the electrocardiogram (ECG). Within the processing of ECG signals, one of the most critical aspects is the interpretation and acquisition of the QRS complex. Both the diagnosis of cardiac rhythm abnormalities and the assessment of heart rate variability depend heavily on the R wave (HRV). In this paper, the modified Pan-Tompkins method with an FIR filter is used for baseline wandering and noise reduction. Temporal statistical features are extracted using the QRS peak detection algorithm. The QRS interval, RR peak interval, QRS deviation, kurtosis, and skewness of the QRS are utilized as the features extracted from the ECG under test. This paper compares the classification accuracy of the standard QRS interval thresholding-based binary approach and the proposed support vector machine (SVM)-based classification method. The proposed optimized K-nearest neighbors (KNN) algorithm outperforms tree and thresholding-based classifiers.

Diabetes is a chronic condition marked by elevated blood sugar levels and presents a growing global health concern. Early and accurate prediction is essential for effective management, enabling timely intervention and reducing strain on healthcare systems. This study investigates the use of ensemble learning techniques to enhance diabetes prediction. Ensemble learning, which combines the strengths of multiple machine learning models, is employed to improve predictive accuracy and reliability. The paper provides a comprehensive overview of key ensemble methods, including bagging, boosting, and stacking, and evaluates their effectiveness in comparison to individual models. Results demonstrate that ensemble approaches consistently outperform standalone algorithms, offering a more robust and dependable framework for diabetes prediction. These findings highlight the significant potential of ensemble learning in medical diagnostics and its critical role in advancing predictive healthcare technologies.