Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES
PaperId: :  21784
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 5 2023
DUI:    16.0415/IJARIIE-21784
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
NITHESH KANNA SBANNARI AMMAN INSTITUTE OF TECHNOLOGY
MAHAVISHNU GBANNARI AMMAN INSTITUTE OF TECHNOLOGY
MITHHULL B BANNARI AMMAN INSTITUTE OF TECHNOLOGY

Abstract

ARTIFICIAL INTELLEGENCE AND MACHINE LEARNING
Thalassemia diagnoses, Relu, SoftMax, TensorFlow, SHAPE.
This study proposes a workflow for preparing and training a neural network model to classify medical data related to thalassemia diagnoses. Thalassemia is a genetic blood disorder that affects the production of hemoglobin, leading to anemia and other complications. Early diagnosis and treatment are essential for improving the quality of life and survival of patients. However, conventional methods of diagnosis are often invasive, expensive, and timeconsuming. In this paper, we propose a novel workflow for preparing and training a neural network model to classify medical data related to thalassemia diagnoses. Our workflow consists of four main steps: data loading, cleaning, and processing; data splitting and oversampling; feature scaling; and model definition, compilation, and training. We use TensorFlow to implement our neural network model, which has several dense layers with ReLU activation functions, dropout regularization, and a SoftMax output layer. We evaluate our model on a publicly available dataset of 420 patients with different types of thalassemia. Our results show that our model achieves an accuracy of 96.67% on the test set, outperforming previous methods based on logistic regression, decision tree, and support vector machine. We conclude that our workflow provides a comprehensive pipeline for data preprocessing and training a neural network model for thalassemia diagnosis classification, addressing class imbalance and ensuring proper data preparation.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. "THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 5 2023 Page 1795-1807
MLA NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. "THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES." International Journal Of Advance Research And Innovative Ideas In Education 9.5(2023) : 1795-1807.
APA NITHESH KANNA S, MAHAVISHNU G, & MITHHULL B. (2023). THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES. International Journal Of Advance Research And Innovative Ideas In Education, 9(5), 1795-1807.
Chicago NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. "THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 5 (2023) : 1795-1807.
Oxford NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. 'THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, 2023, p. 1795-1807. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/THALASSEMIA_PREDICTION_USING_HYBRID_MACHINE__LEARNING_APPROACHES_ijariie21784.pdf (Accessed : 27 March 2025).
Harvard NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. (2023) 'THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES', International Journal Of Advance Research And Innovative Ideas In Education, 9(5), pp. 1795-1807IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/THALASSEMIA_PREDICTION_USING_HYBRID_MACHINE__LEARNING_APPROACHES_ijariie21784.pdf (Accessed : 27 March 2025)
IEEE NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B, "THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, pp. 1795-1807, Sep-Oct 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/THALASSEMIA_PREDICTION_USING_HYBRID_MACHINE__LEARNING_APPROACHES_ijariie21784.pdf [Accessed : 27 March 2025].
Turabian NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. "THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 5 (27 March 2025).
Vancouver NITHESH KANNA S, MAHAVISHNU G, and MITHHULL B. THALASSEMIA PREDICTION USING HYBRID MACHINE LEARNING APPROACHES. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 27 March 2025]; 9(5) : 1795-1807. Available from: https://ijariie.com/AdminUploadPdf/THALASSEMIA_PREDICTION_USING_HYBRID_MACHINE__LEARNING_APPROACHES_ijariie21784.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 3/27/2025 6:47:46 AM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
FLAP-AI BIRDArtificial Intelligence and Machine LearningRamya BN Download
AL-Powered healthcare chatbot for personalized medical assistance and wound classificationAI-Powered Healthcare SystemProf. Manjunath N Download
AI-Powered Resume Generation: A Streamlit-Based System Using PyTorch for Intelligent Career ProfilingComputer EngineeringAditi Meshram Download
AI based object color detection and sorting automationRobotics and AutomationBHUVAN THEJASWI T Download
Advanced knowledge on Synthetic Data Generation Using GANs for Medical Imaging ApplicationsArtificial IntelligenceRajeev Ranjan Download
Information on Cognitive AI Assistants for Visually Impaired Navigation SupportComputer ScienceNagaraj.H Download
Speech Emotion AI for Mental Health Monitoring in Call CentersComputer ScienceGowtham Kumar Download
Multi-Agent Reinforcement Learning for Coordinated Drone SwarmsComputer ScienceDeepak Gowda Download
AI-Based Soil Health Monitoring Using Remote Sensing and Deep LearningArtificial IntelligenceChethan Nagraj Download
Dynamic Criminal Network Link Forecasting via Deep Reinforcement LearningDeep LearningSrikant Tayade Download
Virtual Try-On-Enhancing Fashion Exploration for Gen-ZDeep Learning & Generative ModelsAnushka Mane Download
To Develop an EMG-Based Expert System for the Detection of Wrist Flexion and ExtensionArtificial Intelligence and Data ScienceRohit Mehta Download
LUNG CANCER PREDICTION AND DETECTION USING MLCOMPUTER SCIENCE & ENGINEERINGRASHMI T N Download
NewsTrust: AI-Driven News Verification with Social Publishing FeaturesArtificial IntelligenceRaj Mehta Download
Deepfake Technology: A Cybersecurity Threat and Counter Measures.Computer Engineering (Data Science)Chinmay Jitendra Dhamangaonkar Download
12