Assessment of Neurological Disorders among Children using Machine Learning Techniques | Chapter 10 | Research Developments in Science and Technology Vol. 10

 

 The early diagnosis of neurological problems in children helps medical personnel to enhance the patients' health. Therefore, it is essential to recognise neurological anomalies since, if treatment is delayed, they might turn into major problems. Medical data may be analysed and the problem can be accurately diagnosed with the help of machine learning algorithms. This research has discovered machine learning algorithms on several accuracy metrics to accurately detect three prevalent neurological disorders. A neurological data set is collected from a neuro clinic facility in order to evaluate the efficacy of machine learning approaches. Numerous psychological examinations, including clinic neuropsychiatric observation, audio evaluation, and intellectual coefficient assessment, are also carried out on people who have neurological diseases. Some of the collected characteristics were found to be crucial for figuring out the problem. The findings unmistakably demonstrate that the chosen ML techniques produced results that were more accurate, and there is just a little variation in how well they performed.

Author(s) Details:

G. Reshma,
Department of Information Technology, PVPSIT, Kanuru, Vijayawada, India.

P. V. S Lakshmi,
Department of Information Technology, PVPSIT, Kanuru, Vijayawada, India.

Please see the link here: https://stm.bookpi.org/RDST-V10/article/view/7723  

Clustering Based Feature Data Selection Technique Algorithm for High Dimensional Data: A Novel Approach | Chapter 04 | New Visions in Science and Technology Vol. 7

 Identifying a subset of the most valuable features that gives the same results as the whole collection of features is what feature selection entails. Both the efficiency and effectiveness of a feature selection method can be evaluated. While efficiency is concerned with the amount of time it takes to locate a subset of features, effectiveness is concerned with the subset's quality. Based on these criteria, this study presents and tests FAST, a fast clustering-based feature selection approach. The FAST algorithm is split into two parts. In the beginning, graph-theoretic clustering methods are employed to divide characteristics into clusters. In the second stage, the most representative feature from each cluster that is highly associated to target classes is picked to create a subset of features. FAST's clustering-based technique is expected to yield a subset of valuable and independent features since the attributes in separate clusters are relatively independent. To assure FAST's efficiency, we adopt the efficient Minimum-spanning tree clustering approach. The FAST algorithm's efficiency and efficacy are evaluated through an empirical investigation. Before and after feature selection, four types of well-known classifiers, including the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER, are compared to FAST and several representative feature selection algorithms, such as FCBF, ReliefF, CFS, Consist, and FOCUS-SF. According to the findings, which were based on 35 publicly accessible real-world high-dimensional image, microarray, and text data, FAST not only delivers smaller subsets of features but also improves the performances of the four types of classifiers.

Author(S) Details

Amos R
Department of MCA, MIT Mysore, India.

Kowshik N
Department of MCA, MIT Mysore, India.

Suraksha M. S
Department of MCA, MIT Mysore, India.

View Book:- https://stm.bookpi.org/NVST-V7/article/view/4410