Heart disease prediction

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hey there Glane here, I can help build an end-to-end deep learning pipeline for heart disease prediction from genomic data, covering preprocessing, SNP feature engineering, dimensionality reduction, class imbalance handling, model training, and evaluation with a strong focus on maximizing F1 Score. Using PyTorch or TensorFlow/Keras, I’ll develop a reproducible, GPU-ready workflow with clean, modular code, saved model weights, and comprehensive evaluation including F1, precision, recall, and confusion matrices. I also have experience with biomedical AI projects and can provide architecture justification, hyperparameter tuning, and clear documentation to support future deployment and research extensions.

@shadabkhan92

Done similar tasks Our work with both LLM integrations and predictive ML models is well-aligned with the requirements of your project. Also, we have a vast experience in dealing with Python, the framework for your deep learning model. One of our core strengths is the ability to merge different complementary technologies in unique ways, be it integrating AI and IoT hardware or deploying AI solutions in existing ERP infrastructures. In your case, predicting heart disease from genomic data requires identifying patterns from high-dimensional SNP data which is exactly what we excel at: understanding complex datasets and designing tailored machine learning models that fit them. More importantly, as you mentioned readability and modularity are imperative for you, our code writing style aligns perfectly with these requirements. In conclusion, our extensive expertise in Machine Learning, Python along with strong focus on precision (as demanded by F1 score) make us an ideal fit for your heart disease prediction task. We not only develop accurate models but ensure they are GPU ready, well-documented and reproducible using clear versioning mechanism. Let's interconnect the potentials of genomic data analysis with our intelligent infrastructure to build an impactful prediction system. Looking forward to be a part of this journey!

@nrajuu07

I'm a deep learning specialist with an MS by Research in Medical Image Analysis from IIIT Bangalore and hands-on experience in genomic ML applications. I'll build an end-to-end PyTorch/TensorFlow pipeline for heart disease detection from genetic profiles — handling high-dimensional SNP preprocessing, normalization, dimensionality reduction, feature engineering, class-imbalance techniques (SMOTE, weighted loss), and rigorous train/validation/test splits optimized for F1 Score as the primary metric, balancing precision and recall. Deliverables include clean, modular, fully reproducible Python notebooks loading raw genetic data through model training and prediction, saved versioned model weights with retraining/fine-tuning README, comprehensive evaluation report with F1, precision, recall, and confusion matrix on held-out test set, and clear architecture rationale explaining layer choices, regularization strategies, and hyperparameters suited to high-dimensional genomic inputs. GPU-ready code throughout with attention to modularity for easy deployment and extension. Ready to start as soon as you share the dataset and phenotype labels.

@TheRSTeam

Hi, I am a data analyst/statistician and Economist with more than 6 years of experience. I can do your project, Please take time to check my profile and then you decide to contact me.

@KolmanOuassou

Hi, I'm a data analyst, statistician, and economist with over six years of experience. I understand the requirements of your project and have the skills to deliver high-quality results. To better tailor my approach, could you please review my profile for more details on my previous work and client feedback. Looking forward to your response. Best regards,

@vishal1145

Your genomic dataset will fail standard deep-learning pipelines if you treat SNPs like tabular features - high-dimensional genetic data requires dimensionality reduction (PCA or autoencoders) and careful handling of class imbalance, or your F1 score will collapse under false negatives. Before architecting the solution, I need clarity on two things: What is your SNP count per patient (10K, 100K, or 1M+ variants)? And what is your positive-to-negative class ratio - if heart disease cases are under 20% of your dataset, we'll need SMOTE or focal loss to prevent the model from just predicting "healthy" for everyone. Here's the technical approach: - GENOMIC PREPROCESSING: Apply variance thresholding to remove low-information SNPs, then use PCA or sparse autoencoders to compress features from 100K+ dimensions to 500-1000 latent representations without losing predictive signal. - KERAS + FOCAL LOSS: Build a feedforward DNN with batch normalization and dropout layers, then replace binary cross-entropy with focal loss to penalize misclassified minority cases and optimize directly for F1 score during training. - STRATIFIED K-FOLD VALIDATION: Split data using stratified sampling to maintain class distribution across folds, then track precision-recall curves and confusion matrices at each epoch to catch overfitting early. - GPU-READY DEPLOYMENT: Package the final model with TensorFlow Serving or ONNX export so you can run inference on new patient profiles without retraining, plus version control with MLflow for reproducibility. I've built 4 clinical ML models (2 for genomic risk prediction) that achieved F1 scores above 0.82 on imbalanced datasets. Let's schedule a 15-minute call to review your SNP encoding format and confirm GPU availability before I start development.

@innovateNsolve

Hello, I hold a PhD in Computer Science with over 25 years of experience in Python, Machine Learning, Deep Learning, and AI research. I have experience developing predictive models for high-dimensional biomedical datasets and understand the unique challenges posed by genomic data, including feature sparsity, class imbalance, and overfitting. My focus will be on building a maintainable and extensible workflow rather than simply producing a trained model. The final solution will be clean, modular, and suitable for future deployment or further research. I look forward to discussing your dataset and developing a robust genomic prediction model that meets your performance objectives. Thank you.

@devanshkhandekar

Hi, I am seasoned Applied ML Engineer(6+ yoe) & I can build an end-to-end GPU-ready deep-learning pipeline for heart-disease prediction from genomic/SNP data,with preprocessing,imbalance handling,model training,F1-focused tuning,saved weights,& a clear evaluation report My approach: -Review raw genomic format,phenotype labels,class balance,missing SNPs,encoding style, train/test requirements -Build preprocessing for SNP encoding,missing-value handling,normalization/standardization,feature filtering,& reproducible train/validation/test split. -Start with strong baselines,then train a deep neural network suited for high-dimensional tabular genomic inputs using dropout,batch normalization,weight decay,& early stopping -Optimize primarily for F1 Score,using threshold tuning,class weights or oversampling -Report F1,precision,recall,confusion matrix,& model rationale in plain language -Deliver modular Python/PyTorch or TensorFlow code,saved model weights,README,& retraining instructions Relevant Experience: -Clinical ML Pipelines:Engineered disease-risk & cardiac anomaly prediction models using patient lab data,addressing class imbalances & leveraging SHAP for explainability -Tabular Data Workflows:Built robust ML pipelines for high-dimensional data featuring strict feature selection,leakage-safe validation,& reproducible notebooks -Medical AI Evaluation:Assessed workflows using clinical-grade metrics (AUROC,F1,sensitivity) to ensure reliability beyond basic accuracy

@monfssf

Hello, • Your project aligns well with my experience in Python-based machine learning workflows, data preprocessing, model training, and performance evaluation. • Comfortable working with high-dimensional datasets, feature engineering, data normalization, train/validation/test splitting, and handling class imbalance using appropriate techniques. • Experience building deep learning models using TensorFlow/Keras and PyTorch, with a focus on reproducible and maintainable workflows. • Understand the importance of F1 Score for imbalanced classification problems and can optimize model performance by balancing precision and recall rather than relying solely on accuracy. • Able to design, train, tune, and evaluate neural network architectures while applying appropriate regularization and hyperparameter optimization techniques. • Can deliver a complete end-to-end pipeline including data preprocessing, model training, prediction generation, evaluation metrics, confusion matrix, and documented results. • Code will be modular, well-documented, GPU-ready, and structured for future retraining or extension. • Deliverables will include trained model weights, training scripts/notebooks, evaluation report, and a concise architecture rationale explaining model design decisions. • Available to start immediately and provide regular progress updates throughout development. • Proposed rate: ₹805 INR/hour. To discuss further project details, please message me through Freelancer chat.

@imranahmadquresh

Hi, I understand you need Machine Learning Expert using Python for Heart disease prediction. I offer my services for this project. I have made many Machine Learning based projects using Python as follows; • Predict Johnson & Johnson data using ARIMA & LSTM. • Stock Price Prediction of Amazon data & bank data using ARIMA & LSTM. • Data analysis of Sensor data using multi-variate Linear Regression, Correlation, Normalization & Regularization. • Handwritten Digit Recognition using Fourier response & SVM polynomial. • Classification of CIFAR-10 using different NN models. • Classification of Sentiment of Movie Review using Logistic Regression. • Classification of London Fire Brigade incidents 2019-2022 data using Decision Tree. • IOT Attacks Prediction using SVM, Decision Tree & Random Forest. • Prediction extent of disease of ECG data using Random Forest & SVM. • Sign Language Recognition using SVM with normalization, standardization & data reduction. • Time Series Price Forecasting using Random Forest. • Classification of dementia disease using XGboost & Logistic Regression. • Classification of Muffin & Cupcake ingredient data using SVM. • Classification of size of person using KNN. • Prediction of Solar Radiance using SVM & Bayesian Ridge. • Regression of Boston & Diabetes using Decision Tree, KNN & NN. • Regression of Wine quality using Random Forest. • Clustering fingerprint images using K-Means. I ensure to complete your project efficiently and on time.

@lalitdeveloper7

Hi, I can develop a complete deep learning pipeline for genomic heart disease prediction using Python, TensorFlow/Keras, PyTorch, Deep Learning, Genomics Data Analysis, Feature Engineering, and Classification Models. The solution will include SNP preprocessing, normalization, handling high-dimensional genetic data, class imbalance treatment, model training, hyperparameter tuning, and evaluation with a strong focus on F1 Score, Precision, Recall, and Confusion Matrix. I have experience building reproducible machine learning and deep learning workflows with clean, modular, and well-documented code. You will receive the complete training pipeline, saved model weights, evaluation report, architecture documentation, and retraining instructions. Please let me know further. Thanks.

@KrisuTechkul

In the realm of powerful AI algorithms and impactful business solutions, our team's expertise stands out. Through a rooted skill set in Data Mining, Data Science, Deep Learning, and Machine Learning (ML), we're well-prepared to engage deeply with your project on heart disease prediction. From the outset, we understand the meticulous preparatory steps this entails: prepossessing genomic data, undertaking high-dimensional SNP handling, normalizing it, balancing classes if needed and dividing into training/validation sets. We also value the importance of F1 Score in your project; accuracy alone won't suffice here. Crafting a solution that balances precision and recall is what we do best. Our extensive experience with PyTorch and TensorFlow/Keras ensures your project will be developed in conducive deep learning frameworks for clean reproducible code and GPU readiness. Coupled with this proficiency promising a well-documented workflow that you can easily deploy or extend means you can look forward to future scalability without any hassle. Furthermore, beyond just delivering the end-to-end solution you seek, we will accompany it with saved model weights for retraining/fine-tuning plus an evaluation report covering all pertinent metrics of interest like F1 Score, precision, recall, and confusion matrix.

@coretus

Heart disease prediction needs more than a generic classifier. With genomic SNP data, the right preprocessing and model design will matter as much as the network itself, especially when F1 score is the target. I can build a clean, reproducible Python workflow that covers: 1. Data loading, SNP preprocessing, normalisation, and train, validation, test splitting. 2. Class-imbalance handling if needed, then deep learning model training in TensorFlow/Keras or PyTorch. 3. Evaluation focused on F1, precision, recall, and confusion matrix. 4. Saved model weights, retraining notes, and a short architecture rationale. Why me: - Strong fit in Python, Machine Learning, Data Science, Data Analysis, Neural Networks, Keras, and Model Deployment. - I keep code modular and documentation clear so you can extend it later. - I can tune layers, regularisation, and thresholds specifically for F1, not just accuracy. Price and timeline: - INR 1200/hr is within your budget and reflects the required deep learning + preprocessing work. - Estimated 8 days to deliver the notebook/code, trained model, and report with one revision round. If you share the dataset structure, I can start with a preprocessing plan and model baseline right away. Coretus Technologies https://www.freelancer.com/u/coretus

@iterativeTech

As an experienced AI and Cloud Data Engineer, I am confident in delivering a solution that meets your project's unique needs. My extensive background empowers me to seamlessly combine data analysis, data engineering, and machine learning into your project to ensure it's not just about implementation, but also about outcome. I have not only worked with PyTorch and TensorFlow/Keras, but also boast a strong understanding of their respective strengths and weaknesses, allowing me to employ the best choice for your specific genomic dataset. Your overriding success metric: F1 Score is at the forefront of my mind throughout this project. Drawing on my experience in finance, healthcare, and insurance industries, I've tackled highly complex projects much like yours. Whether dealing with imbalanced classes or handling high-dimensional SNPs, I possess the skills to create reproducible and GPU-optimized code capable of managing any task thrown at it. The modularity and readability you desire in the final deliverable is where I excel as well. My solutions are always meticulously documented with detailed explanations of design choices like layer selection, hyperparameters tuning, etc., so you can fully understand and effortlessly extend the project in the future. Contact me now to kickstart our collaboration that'll transform your genomic data into actionable insights with tangible business impact

@WiredAI

Hi, This project aligns well with our AI/ML expertise. We recently completed a very similar genomics-based deep learning project under an IP agreement, so I can't share the code publicly, but I'd be happy to discuss the architecture, preprocessing strategy, feature engineering, and model optimization in a private chat. We can build an end-to-end pipeline covering SNP preprocessing, class balancing, deep neural network training, hyperparameter tuning, and F1-score optimization, along with reproducible code, versioned model weights, and detailed evaluation reports. Let's connect to discuss your dataset and objectives—I can also share our approach and experience in this domain. WiredAI Ventures

@Apponn

Hey there, I read your project details and understand that you need a heart disease prediction model with accurate analysis, proper data preprocessing, model training, and performance evaluation. I have experience working on machine learning and data analysis projects and have completed similar predictive modeling tasks before. I can help you build a reliable model, compare different algorithms, and provide clear documentation of the results. I will make sure the project is completed with clean code, accurate predictions, and proper reporting while providing the best solution according to your budget. Thanks Rohit

@Hemakanth56

Dear Partner, I am excited to submit my bid for your genomic deep-learning pipeline. As an Electronic Engineer specializing in machine learning and biomedical signal processing, I am uniquely qualified to build this system. My background directly aligns with your needs: I am the lead researcher developing a non-invasive blood glucose monitoring system utilizing PPG signals and ML (SVR, XGBoost). This experience in high-dimensional biomedical datasets and preparing manuscripts for IEEE journal submission guarantees a rigorous, scientifically validated approach. Strategy & Architecture *Pre-processing: Modular pipeline for high-dimensional SNPs, variant encoding, normalization, and SMOTE/weighted loss. *Architecture: PyTorch/TensorFlow framework using an Embedding Layer for sparse SNPs, dense residual blocks, Batch Normalization, and Dropout to counter overfitting. *F1 Optimization: Custom macro-F1 or focal loss with dynamic threshold tuning to strictly maximize your primary metric. Deliverables & Partnership I will deliver a clean, GPU-ready Python repository with modular scripts, saved weights, and full evaluation reports (F1, precision, recall, confusion matrix).

@ahmadraza001912

Hi, with high-dimensional SNPs you'll want feature selection before the network ever sees the data - PCA or a chi-square filter to cut dimensionality, otherwise it just memorizes noise on a small genomic set. Since F1 is your real metric, I'd tune the classification threshold and use a weighted loss or SMOTE on the minority class instead of trusting accuracy. Stack: - PyTorch, compact MLP or 1D-CNN over SNP encodings, GPU-ready via CUDA - stratified k-fold for train/validation given limited sample size - scikit-learn for the confusion matrix, precision/recall, F1 reporting I work mostly in Python across ML and backend systems and I'm used to keeping code modular for later retraining. You'll get a clean notebook, versioned model weights, and a short README to fine-tune later. Is the genetic data already balanced across classes, or should I plan for SMOTE from the start?