Online ISSN: 2515-8260

Keywords : K-Nearest Neighbor


RoopaRechal. T; P.Rajesh Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1156-1167

Autism Spectrum Disorder (ASD) is a sort of developmental issue of the
nervous system, with center impedances in social connections, creative mind,
communications, adaptability of thought,intrigue andrestricted range of activities.
Examination of electroencephalographic (EEG) signals based on autism is explored in
this work. Even so, it is critical to identify autism by the analysis of the EEG signal.
Hence feature extraction based on the EEG signals takes part a prominent role in
autism recognition. A practical feature extraction technique variational mode
decomposition (VMD) to diagnose autism is narrated in this paper. Further, the features
extracted are fed to classifiers ANN, KNNand SVM to stratify autism.SVM classifier
shows a finer classification performance when compared to extant techniques

Segmentation of Images in Medical Field using Machine Learning Integrated Approaches

Manoj Kumar; Neeraj Varshney

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 54-58

Now a day’s people are mostly affected by tumors. so, the major intend of this paper is to identify the tumor in a body and detecting the nearest area effected and that will be done by using machine learning with region-based energetic contour model, region based active contour model is efficient in dividing images by badly distinct boundaries but frequently not succeed while functional image surrounding intensity in homogeneity. Machine learning approaches are extremely efficient in conducting the homogeneity, but frequently consequences in noises from miss confidential pixels. Therefore, proposed system point out the integration of the machine learning region based active contour of the k-nearest neighbors and the support vector machine with the chanvese technique, and by comparing this result with the traditional technique of chan-vese technique. Better exactness, velocity and less compassion to constraint tuning which are being observed in this paper.

A Study Of Breast Cancer Analysis Using K-Nearest Neighbor With Different Distance Measures And Classification Rules Using Machine Learning.

M.D. Bakthavachalam; Dr.S .Albert Antony Raj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 4842-4851

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.

Exploration Of A State Of The Art On Cardiac Diseases Prediction Techniques

S. Usha; Dr.S. Kanchana

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 6962-6967

Healthcare is a predictable task to wipe out human life. Coronary heart disease is sickness that impacts the human coronary heart. Cardiovascular sicknesses will forecast with the aid of several techniques that helped in making choices about the modifications that maintain excessive-risk patients which resulted in the discount of their dangers. The purpose of demise ratio of those sicknesses may be very high. It is very imperative to become aware of if the individual has heart disorder or now not. In medical field it is very important to find the occurrence of prediction of the heart diseases. Accurate Prediction results are very efficient to treat the patient’s medical history before the attack occurs. The techniques Data mining and Machine learning plays a essential role to predict the occurrence of heart diseases. These techniques diagnose these diseases with the help of dataset in healthcare centers. Various models used to reduce the number of deaths ratio. Models based on several algorithms such as Support Vector Machine (SVM), Decision Tree(DT), Naïve Bayes(NB), K-Nearest Neighbor(KNN), and Artificial Neural Network (ANN) are implemented to predict heart disease. The accuracy of these models helps to diagnose the diseases with better results. This paper summarized the performance of all algorithms which are used to predict and diagnose heart diseases.