European Journal of Molecular & Clinical Medicine

Chronic kidney disease (CKD) is a major problem on the healthcare system because of its high increasing prevalence and poor morbidity. Artificial Intelligence peculating its role in every field of research including healthcare and diagnosis of diseases. Recently, machine learning approaches are applied to raise consciousness about key health hazards including chronic kidney disease (CKD). When kidneys are damaged, they are unable to perform their normal role of filtering blood. Therefore, it is tough to anticipate, recognize, and prevent such a sickness, which may result in long-term health repercussions. Machine learning methods aid in more precise forecasting to tackle this problem at an early stage. With the increase of technology aids, it makes an ambiguity on which algorithm to apply for prediction of CKD. To address these issues several machine learning algorithms such as Logistic Regression, Naive Bayes, and Decision Tree are applied. Experiments are conducted utilizing the rich set of data in a MATLAB environment. Logistic Regression shows potential for reducing mortality from chronic kidney disease by enhancing prognosis and diagnostic accuracy at an early stage

Parkinson Disease (PD) is brain disorder that affects the central nervous system
that results in damage of nerve cells causing dopamine to drop. PD has a severe effect on
vocal features termed as Dysarthria symptoms including varied pitch, extended pauses,
monotonous and speaking slowly or with a slur. In this paper, a dataset containing various
vocal features are taken as input to analyze the performance of various Machine Learning
algorithms including Naive Bayes, Random Forest Classifier, Support Vector Machines
(SVM), Linear Regression, K Nearest Neighbor (KNN) and Neural Networks such as ANN
and LSTM. The best classification accuracy was obtained by ANN around 90.00%.

Breast Cancer is one of the life threatening disease among females all over the world. This killer disease however when it can be detected in its early stages can be a life saver for many. Radiologists uses the mammography images to detect the presence and absence of Breast Cancer. The field of Bio-informatics leverages the Machine learning techniques for diagnosis of Breast cancer in particular. This research work experiments with the two most popularly used Supervised Machine Learning Algorithms, K-Nearest Neighbour and Naive Bayes. This work predicts Breast Cancer on the The Breast Cancer Data Set (BCD) taken from the UCI Machine Learning Repository. A comparative analysis between the two approaches are made in terms of its performance metrics using CV techniques. The proposed work has achieved a best accuracy of 97.15% by employing the KNN algorithm and a lowest error rate of 96.19% using NB classifier.