For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
A
Akachukwu De-King

Akachukwu De-King

Data Labeling for Machine Learning Health Outcome Prediction (Academic Project)

United Kingdom flagLeeds, United Kingdom
$20.00/hrIntermediateScale AIAppen

Key Skills

Software

Scale AIScale AI
AppenAppen

Top Subject Matter

Healthcare/Global Health Outcomes
Healthcare Algorithmic Bias
Image Classification (Deep Learning)

Top Data Types

TextText
ImageImage
VideoVideo

Top Task Types

ClassificationClassification
Text GenerationText Generation
Question AnsweringQuestion Answering
Text SummarizationText Summarization
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
Fine-tuningFine-tuning
Evaluation/RatingEvaluation/Rating

Freelancer Overview

Data Labeling for Machine Learning Health Outcome Prediction (Academic Project). Core strengths include Python and TensorFlow. Education includes Master of Science, University of Hull (2025) and Bachelor of Pharmacy, University of Nigeria Nsukka (2022). AI-training focus includes data types such as Text and Image and labeling workflows including Classification.

IntermediateEnglishIgbo

Labeling Experience

Image Data Annotation for CNN Classification (Academic Project)

ImageClassification
I performed image data annotation and classification using deep learning methods on a TensorFlow-based pipeline. Tasks involved preparing and labeling image datasets for supervised convolutional neural network training and verifying model outputs for feature extraction accuracy. The project required continually refining image labels to optimize learning performance and model robustness. • Labeled and curated raw image data for use in CNN models. • Verified classification outcomes and corrected mislabeled samples. • Iteratively adjusted image label assignments to enhance feature mapping. • Assured consistency in labeling standards for deep learning workflows.

I performed image data annotation and classification using deep learning methods on a TensorFlow-based pipeline. Tasks involved preparing and labeling image datasets for supervised convolutional neural network training and verifying model outputs for feature extraction accuracy. The project required continually refining image labels to optimize learning performance and model robustness. • Labeled and curated raw image data for use in CNN models. • Verified classification outcomes and corrected mislabeled samples. • Iteratively adjusted image label assignments to enhance feature mapping. • Assured consistency in labeling standards for deep learning workflows.

2023 - Present

Bias/Fairness Data Annotation in Healthcare AI (Academic Review Project)

TextClassification
I deeply analyzed model outputs and algorithmic bias in healthcare AI systems. The core labeling activity focused on reviewing, categorizing, and annotating evidence of bias and fairness in model predictions based on Black/White patient subgroups. I also synthesized proposals for improved labeled concepts and advocated federated learning as an annotation and training approach. • Annotated instances of documented AI bias in existing models. • Classified sources of bias and interpretability trade-offs. • Developed conceptual labels for intermediate medical features. • Proposed mechanisms for privacy-preserving collaborative labeling.

I deeply analyzed model outputs and algorithmic bias in healthcare AI systems. The core labeling activity focused on reviewing, categorizing, and annotating evidence of bias and fairness in model predictions based on Black/White patient subgroups. I also synthesized proposals for improved labeled concepts and advocated federated learning as an annotation and training approach. • Annotated instances of documented AI bias in existing models. • Classified sources of bias and interpretability trade-offs. • Developed conceptual labels for intermediate medical features. • Proposed mechanisms for privacy-preserving collaborative labeling.

2023 - Present

Data Labeling for Machine Learning Health Outcome Prediction (Academic Project)

TextClassification
I reviewed WHO datasets and applied supervised and unsupervised learning algorithms to identify patterns and predict health outcomes. The primary task involved preparing, cleaning, and structuring data for machine learning model training and evaluation. Key labeling tasks included normalization and categorization of features, along with clustering assignments for economic groups among countries. • Implemented and validated data preparation for supervised/unsupervised machine learning models. • Assigned cluster labels to countries based on economic and health profiles. • Normalized continuous variables and encoded categorical variables for accuracy improvement. • Ensured annotative integrity for the use of external, sensitive health datasets.

I reviewed WHO datasets and applied supervised and unsupervised learning algorithms to identify patterns and predict health outcomes. The primary task involved preparing, cleaning, and structuring data for machine learning model training and evaluation. Key labeling tasks included normalization and categorization of features, along with clustering assignments for economic groups among countries. • Implemented and validated data preparation for supervised/unsupervised machine learning models. • Assigned cluster labels to countries based on economic and health profiles. • Normalized continuous variables and encoded categorical variables for accuracy improvement. • Ensured annotative integrity for the use of external, sensitive health datasets.

2023 - Present

Education

U

University of Nigeria Nsukka

Bachelor of Pharmacy, Pharmacy

Bachelor of Pharmacy
2016 - 2022
U

University of Hull

Master of Science, Data Science and Artificial Intelligence

Master of Science
2025

Work History

S

Scale Ai

Ai trainers

Leeds
2025 - 2026
A

Appen

Search engine evaluator

Abuja
2020 - 2023