Diagnosing Cancer Using IOT and Machine Learning Methods

Abstract

Breast cancer affects one in every eight women and is the most common cancer. Aim. To diagnose breast cancer, a potentially fatal condition, using microarray technology, large datasets can now be used. Methods. This study used machine learning algorithms and IOT to classify microarray data. They were created from two sets of data: one with 1919 protein types and one with 24481 protein types for 97 people, 46 of whom had a recurring disease and 51 of whom did not. The apps were written in Python. Each classification algorithm was applied to the data separately, without any feature elimination or size reduction. Second, two alternative feature reduction approaches were compared to the first case. In this case, machine learning techniques like Adaboost and Gradient Boosting Machine are used. Results. Before applying any feature reduction techniques, the logistic regression method produced the best results (90.23%), while the Random Forest method produced good results (67.22%). In the first data, SVM had the highest accuracy rate of 99.23% in both approaches, while in the second data, SVM had the highest rate of 87.87% in RLR and 88.82% in LTE. Deep learning was also done with MLP. The relationship between depth and classification accuracy was studied using it at various depths. After a while, the accuracy rate declined as the number of layers increased. The maximum accuracy rate in the first data was 97.69%, while it was 68.72% in the second. As a result, adding layers to deep learning does not improve classification accuracy.

A K -Means Algorithm-Based Method in IOT Applications for Evaluating the Effectiveness of Physical Exercise in Preventing Youth Rebellion

“…Different practices (aspects) of corporate social responsibility are practiced in the operation of companies in Kosovo, depending on what managers consider to be of particular importance to their company [7]. The aspects that refer to the employees and their rights are practiced the most (37.50%), followed by the ethical behavior of all staff members (22.50%), followed by the practice of high standards of corporate governance (17.50%), practices related to environmental protection (12.50%), philanthropy and charitable giving (7.50%), and others (2.50%) (Table 5).…”

Intelligent Fuzzy Optimization Algorithm for Data Set Information Clustering Patterns Based on Data Mining and IoT

“…As a result, data mining's primary purpose has switched to getting more valuable Internet information and data [24]. There has not been a precise standard for data mining discipline till recently [24][25][26]. Data mining (DM) is defined as observing items that are essential to clients from large databases or data frameworks and removing and breaking down connections that clients make will not handily distinguish or attest from colossal perception.…”

Neural Network Based on Health Monitoring Electrical Equipment Fault and Biomedical Diagnosis

“…Because of the redundancy between data, the spatial vector model is established by selecting feature words, and the original data are processed by the heuristic attribute reduction algorithm of principal components [ 27 , 28 ], and the optimal selection is made to complete the data preprocessing. First, we need to extract fault feature words from fault statements, and the calculation formula is as follows: …”