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

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@shadabkhan92

As an AI systems builder with a focus on predictive ML models like the one your project entails, I am well-positioned to tackle your heart disease prediction task. With expertise in deep-learning frameworks such as PyTorch and TensorFlow/Keras, I will not only handle the intricate aspects of your dataset like high-dimensional SNPs but also ensure that your model is finely tuned to cater for class imbalance through robust preprocessing and feature engineering strategies. Critical to the success of your project is the achievement of a high F1 Score. In consonance with this, my team and I take your emphasis on precision and recall seriously. With us, you can expect a comprehensive evaluation report outlining F1 Score, precision, recall, and confusion matrix metrics on your held-out test set for a holistic view of our model's performance. Finally, our ability to build not just prototypes but production-grade infrastructure sets us apart. We are ardent advocates for clean, reproducible code which can be easily understood and maintained by other stakeholders. The well-documented nature of our workflow is designed to empower users to effectively deploy or extend the model independently. Let's bring AI into your existing workflows; let's build a system that works!

@edulov

Hello My name is Eugene and I have the profile and experience you need. I do not have formal medical education but I worked a lot with medical situations, so I can handle you data not only mathematically. Data pre-processing and development of the custom prediction model will be aimed to ensure the best possible accuracy. I am working fast and can start this project immediately. Best regards,

@amirrmusavifr

Hi, I'm a Python specialist with the necessary skills to complete your project. I have skill sets for: • Analyze data sets using Python and the specified libraries. • Provide insights and visualizations based on the analysis. • Clean and preprocess data to ensure accuracy and reliability. • Develop custom Python functions/scripts to automate routine tasks. • Generate detailed reports to summarize findings and present data insights. • Ensure integrity and accuracy of data throughout the analysis process. • Support hypothesis testing and statistical analyses for data-driven decisions. • Create compelling data visualizations using libraries such as Matplotlib and Seaborn. • Identify and interpret data trends and patterns for predictive analysis. • Optimize and streamline data analysis workflows for efficiency. • Familiarity with Kaggle datasets and tools. I already worked on Time Series of data specifically with the use of Pandas and NumPy for Sequence of data includes Automated stock trading, Stock prices, Weather and classification on series data. My track record of success with similar projects is proof that I can deliver results quickly and accurately. If you're interested in hearing more about how I could help you, please don't hesitate to reach out! I can provide the requirements with minimum time and cost.

@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, feature engineering, class-imbalance techniques, and rigorous train/validation/test splits optimized for F1 Score (not accuracy alone). Deliverables include clean, modular, fully reproducible Python notebooks, 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, and hyperparameters suited to genomic data. GPU-ready code throughout. Ready to start as soon as you share the dataset and phenotype labels.

@ashinnd84

Hello there, we are a team of Full Stack Web and Mobile App Developers and we can do this project in no time. Please, send me a message to discuss the work. Thanks Ashish Kumar.

@pebambang5

Hello, I have read the outline of your project ("I have done similar project"), and I’m sure can solve the task, provide correct result, free revision guarantee. My background is in statistics and applied mathematics using Python/R/JS programming for model statistics, predictive analytics, machine learning and artificial intelligence. I’m an expert in various model regression, hypothesis analysis, Biostatistics, Image/Cancer analysis, use PyTorch, TensorFlow, provide in Python notebook/Django/Flask, markdown file html/pdf/word, complete with the charts. Please share your data, I'm available, discuss detailed requirements, budget/time negotiable. Thank you. Best rgds, Bambangpe

@KrisuTechkul

With a profound understanding of data mining and processing, I am confident in delivering an exceptional deep-learning model for your heart disease prediction project. My comprehensive skills in machine learning and deep learning including Python, align perfectly with the nuances in your project description, enabling me to effectively preprocess and engineer features within your genomic dataset to maximize the F1 Score - your single success metric which I wholeheartedly endorse. Throughout my career, I have specialized in carving impressive AI tools within healthcare, a proficiency that overlaps neatly with your project's core requirements. My expertise with predictive analytics encompasses not just analyzing medical images for early disease detections but also using machine-learning systems to detect and prevent fraud in real-time - embodying complexities in the medical field. This experience equips me with the flexibility and adaptability that are critical when deploying state-of-the-art algorithms for unique projects such as yours.

@bizvivo

With my extensive skills in Biostatistics, Data Science and Analytics, I am supremely poised to tackle your project. Over the years, I have built incredible proficiencies in machine learning and big data technologies, making me a perfect fit to preprocess and perform well-crafted feature engineering on your large genomic dataset. My ability to transform complex data sets into actionable insights has been instrumental in driving increased ROI and efficiency for businesses. I understand the criticality of having an effectively-tuned model for your project. Hence, precision and recall will be treated with equal importance as accuracy throughout the process. I am proficient in both TensorFlow/Keras as well as PyTorch frameworks, so rest assured that you will get a clean, reproducible, and GPU-ready code with results that you can easily interpret. Additionally, I plan to provide you with more than just model weights. You can expect a comprehensive README on how to retrain or fine-tune the model in the future; an evaluation report highlighting F1 Score, precision, recall and confusion matrix; as well as a brief architecture rationale explaining exactly why the layers, regularization techniques, and hyperparameters were chosen to suit your genomic input requirements. Let's talk about your project further and create a robust solution that not only meets but surpasses your expectations!

@FreeLancers360

As a part of the growing team at NCommand Web Services, I believe we have the ideal skillset to tackle your project and provide you with an innovative approach to deep-learning using Python. Our collective proficiency in HTML, CSS, JavaScript, NodeJS, ReactJS, PHP, and Laravel ensures that we fully understand the needs of preprocessig your genomic data efficiently and effectively. With our strong background in creating websites using both front-end and back-end technologies, we guarantee meticulousness in handling your high-dimensional SNPs and normalizing your data. Our Python prowess certainly also extends to PyTorch and TensorFlow/Keras as mentioned in your project description. We've had significant experience leveraging these frameworks for deep learning projects with great success. We'll make sure the model we build specifically prioritizes F1-score over mere accuracy by fine-tuning it diligently to balance precision and recall. As you've indicated that reproducibility is important to you, we'll ensure the final code is clean, well-documented and easily scalable for GPU-readiness.

@Abdulyaqs

I can build your end-to-end deep learning solution for genomic heart disease prediction with F1 Score as the primary metric. My Approach: Preprocessing: Handle high-dimensional SNPs with normalization, train/validation split, and class-imbalance techniques (weighted loss, oversampling) Feature Engineering: Feature selection to identify most informative SNPs, reducing dimensionality while preserving predictive power Deep Learning Model: PyTorch-based neural network with batch normalization, dropout regularization, and hyperparameter tuning F1 Optimization: I will tune the model to balance precision and recall, using early stopping and learning rate scheduling Deliverables: End-to-end Python code (clean, modular, GPU-ready) Saved model weights and retraining instructions Evaluation report: F1 Score, precision, recall, confusion matrix Architecture rationale with layer/regularization explanations Why Me: ML and feature selection expertise from previous projects Optimization mindset for hyperparameter tuning Clean, documented code (Choice Crafter, OptiDash) 6 published papers – I know how to document technical work Please share the dataset and I will start immediately. Best regards, Yakubu Abdullahi

@dushyant317

Hi, I can develop a complete deep-learning pipeline for genomic heart disease prediction, including SNP preprocessing, feature engineering, class-imbalance handling, model training in PyTorch/TensorFlow, hyperparameter optimization focused on maximizing F1 Score, comprehensive evaluation (precision, recall, confusion matrix), and delivery of clean, modular, GPU-ready code with saved model weights and full documentation for future retraining and deployment.

@NEHABHAT92

Being a web and mobile development company, Python is our game changer. We have honed our skills in Python for over 9+ years. With profound experience in E-commerce and content management system based websites, we completely understand the vitality of clean, well-documented code and a modular architecture for your project. Leveraging this knowledge, with complete reproducibility, we will deliver you an end-to-end Python code or notebook that handles the high-dimensional SNPs, performs preprocessing and trains the deep neural network for your genetic data. As requested, accuracy isn't our single metric but a balance between precision and recall i.e; F1 Score. We have extensive experience in tuning models to provide optimal performance by using specific layers, regularisation techniques and hyperparameters suiting the demands of genomic inputs. This adaptability allows us to work using either PyTorch or TensorFlow/Keras. Moreover, we believe in providing long-term client satisfaction which is why we commit three months of post-delivery free support. Lastly, as cost-effectiveness is important to you, we assure you that our services won't break your bank while providing top-notch quality work. Let's team up to turn this exciting idea into a reality!

@Marwan328

Hi, I reviewed the attached analysis I can see you're working with the Heart Failure Prediction dataset (1,025 patients, 13 clinical features) and have already benchmarked SVM, Random Forest, Decision Tree, and Logistic Regression, with SVM reaching 99% F1. For the deep learning extension, I'd build a PyTorch MLP with BatchNorm and Dropout, tuned specifically for F1 rather than accuracy, using the same preprocessing pipeline (Box-Cox for outliers, one-hot encoding for cp/thal/restecg, stratified split). I'd also address the limitations your analysis flagged proper held-out test evaluation and cross-validation to confirm generalizability beyond the training distribution. Deliverables: clean reproducible notebook, versioned model weights + README, and evaluation report with F1, precision, recall, and confusion matrix. Ready to start immediately.

@Apponn

Hi there, I read through your requirements and understand the project. You need a deep learning model that can predict heart disease from genetic data, with a strong focus on achieving the best possible F1 Score through proper preprocessing, feature engineering, and model tuning. I have experience working with Python, machine learning, deep learning, and data preprocessing. I can build a clean, well-documented pipeline that handles the full workflow from data preparation and training to evaluation and model deployment. I would also recommend testing multiple feature selection and class-balancing approaches, as they can significantly improve F1 Score when working with high-dimensional genomic data. I can provide the best solution for your project and would be happy to connect and discuss the details. 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 *Preprocessing: 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).

@shubhankar1967

Hi, I am an IITian with 10+ years of experience to develop similar projects ,I will build a PyTorch-based feedforward neural network with dropout and batch normalization layers, preprocessing SNP data using scikit-learn's VarianceThreshold for feature selection, StandardScaler for normalization, and SMOTE or class-weighted loss to address imbalance. Training will use stratified k-fold cross-validation with early stopping, optimizing for F1 score via precision-recall threshold tuning on a held-out validation set, and I'll track experiments with Weights & Biases. Kindly click on the chat button so I can share you my relavant projects. Lets connect

@sanddep5

With a deep-rooted passion for data analysis and science, I am confident that I can deliver the heart disease prediction model you seek. My proficiency in Python, paired with extensive experience in data analytics is an ideal combination for this project. I have handled datasets of diverse complexities and have a robust understanding of preprocessing and feature engineering techniques, which are integral to amplifying the performance of any deep learning model. I assure you absolute diligence in handling your high-dimensional genomic data, employing effective normalization and class-imbalance techniques if required to create an unbiased model. You emphasized the importance of F1 Score as your main success metric, which sets stern standards for both precision and recall. Rest assured, I'll deliver a well-tuned model that balances both with utmost precision. I am comfortable working with either PyTorch or TensorFlow/Keras thus the choice remains at your discretion. However, irrespective of the framework chosen, you can trust me to deliver clean, reproducible code optimized for GPU usage. Moreover, my ultimate priority aligns perfectly with yours - creating a clear and well-documented workflow that's easily deployable or extendable in the future.