1 / 8

Decision Trees vs Black-Box Models in Indian Healthcare

Discover how Decision Trees and Black-Box models impact healthcare decisions in India. Learn why interpretability matters, especially if you're exploring a machine learning course in Hyderabad.<br><br>

sonu83
Download Presentation

Decision Trees vs Black-Box Models in Indian Healthcare

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Decision Trees vs Black-Box Models in Indian Healthcare ● Why Interpretability Matters ● Machine Learning Course in Hyderabad

  2. Introduction to ML in Healthcare ● AI is transforming healthcare globally, including India ● Accuracy matters—but so does interpretability ● Decision Trees offer transparency; Black-Box models do not ● Relevance: Especially important for learners taking a machine learning course in Hyderabad

  3. What Are Decision Trees? ● Supervised ML algorithm (classification/regression) ● Mimics human decision-making ● Every prediction is traceable and explainable ● Best used in: Diagnosis support, triage systems, and early detection

  4. What Are Black-Box Models? ● Includes deep learning, ensemble methods, etc. ● High accuracy but low transparency ● It is difficult to explain how decisions are made ● Used in complex tasks (e.g., image-based diagnostics)

  5. Interpretability in Indian Healthcare ● Crucial in clinical, ethical, and legal settings ● Doctors, regulators, and patients demand transparency ● Public hospitals and rural India require low-tech, interpretable tools ● Decision Trees help build trust and enable adoption

  6. Case Study: Heart Disease Prediction (AP) ● Decision Trees used in a pilot project in Andhra Pradesh ● Helped predict heart risks using patient profiles ● 30% higher adoption than black-box models ● Model accepted due to transparent decision-making path

  7. What ML Learners Should Know ● Decision Trees are foundational for learning ensemble models ● A good machine learning training in Hyderabad should include: Ethical AI use Healthcare use cases Explainable AI (XAI) tools like SHAP, LIME ● Consider machine learning course fees in Hyderabad in relation to training quality

  8. Conclusion & Takeaways ● Interpretability is non-negotiable in Indian healthcare ● Decision Trees offer explainability, trust, and ease of use ● Black-box models have niche uses, but need explainers ● Aspiring ML professionals should master both for career-ready AI skills

More Related