Prediction of breast cancer using machine learning algorithms on different datasets

Abstract

Breast cancer is a disease that is becoming more and more common day by day, causing emotional and behavioral reactions and having fatal consequences if not detected early. At this point, traditional methods are insufficient, especially in early diagnosis. In this context, this study aimed to predict breast cancer by using machine learning (ML) algorithms on different datasets and to demonstrate the applicability of these algorithms. Algorithm performances were compared on balanced and unbalanced datasets, taking into account the performance metrics obtained in applications on different datasets. In addition, a model based on the Borda Voting method was developed by including the results obtained from four different algorithms (NB, KNN, DT, and RF) in the process. The prediction values obtained from each algorithm were written in different columns on the same excel file and the most repetitive value was accepted as the final result value. The developed model was tested on real data consisting of 60 records and the results were analyzed. When the results were examined, it was seen that higher performance was obtained with the proposed RF model compared to similar studies in the literature. Finally, the prediction results obtained with the developed model revealed the applicability of ML algorithms in the diagnosis of breast cancer.

Hidden Markov Models for early detection of cardiovascular diseases

“…HMMs are probabilistic models that describe observable events that depend on unobservable internal factors [2], based on the Markov property that the probability of transitioning to a future state depends only on the current state and not on previous states. Their applications have been diverse, encompassing facial recognition, speech recognition, genetic prediction, bioinformatics analysis, automatic classification of electrocardiograms (ECGs), neuronal activity in the visual cortex, epileptic seizures, tracking the movement of living organisms using ultrasound, aligning protein structure sequences, detecting homogeneous segments in DNA sequences, learning non-singular phylogenies, genetic mutation analysis of HIV sequences, and human genome analysis [3]- [5].…”