An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
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Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 6 | Issue - 1 | march 2024
Published
26 March, 2024
Malignancy risks and genetic disorders have long been challenging due to procedures that lack precision and predictability, thereby complicating the precise identification of diseases and their root causes. Machine learning classifiers have emerged as more suitable and effective tools. Various machine learning classifiers have been utilized to examine different genetic disorders, and the results from these classifiers have been further compared to determine their superiority. In this study, a variety of classifiers, including the SVM, KNN, decision tree, random forest, and logistic regression algorithms, are examined. These classifiers utilize specific training variables to analyze how input values correspond to the respective class. After successfully implementing each classifier, we proceeded to employ Stacking, an ensemble machine learning technique that aggregates predictions from individual classifiers on the same dataset. Four datasets, including the breast cancer, diabetes, Parkinson’s, and genomic datasets, were successfully implemented using the aforementioned methods, and the results obtained showed how the input values correspond to the class using a few training variables. SVM classifier was shown to be the most effective of the five described classifiers, having the highest accuracy in most of the cases. It provided accuracies of 97.43%, 97.46%, 97.45%, and 97.44% for each of the genome cancer, diabetes, Parkinson’s, and breast cancer datasets. The KNN and Random Forest models also came out to be very effective, with accuracy around 95% and 91%, respectively, for various disease datasets. The Logistic Regression and Decision Tree models also worked well. However, the ensemble method of Stacking proved to be highly efficient above all other base models and generated accuracies above 97.5% for all the aforementioned diseases.
KeywordsMachine Learning Classifiers Disease Detection SVM KNN Decision Tree Random Forest Logistic Regression Algorithms Stacking
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