The problem HEALTHCARE.AI solves

Problem Statement

The Diagnostic Dilemma in Healthcare

● Challenge: Current medical diagnostic processes suffer from a substantial error rate, leading to misdiagnoses
and suboptimal patient outcomes.
● Stats: According to a study by Johns Hopkins Medicine1, diagnostic errors contribute to around 10% of patient
deaths in the U.S. annually.

The Time Factor: Delays in Diagnosis

● Challenge: Traditional diagnostic methods are time-consuming, causing delays in initiating crucial treatments.
● Stats: The National Academy of Medicine2 highlights that it takes an average of 17 years for a medical discovery
to be fully integrated into clinical practice.

Inaccurate Diagnoses: Hidden Costs

● Challenge: Misdiagnoses result in prolonged treatments, increased healthcare expenses, and avoidable
hospitalizations.
● Stats: A report by BMJ Quality & Safety3 estimates that diagnostic errors cost the U.S. healthcare system over
$700 billion annually

Challenges we ran into

Dependencies installation during deployment, silly errors while backend integration using flask

Ensuring the dataset is accurate: The dataset might contain errors or inconsistencies that need to be identified and corrected.

Handling missing values: Dealing with missing data requires decisions on imputation or removal, which can affect the model's performance.