AI MEDICAL SYSTEM v2.0 CTG Analysis & Diagnosis

Fetal Health Intelligence

Intelligent Fetal Health Diagnosis System Using Machine Learning and Medical Data Analysis

Model Accuracy

93.19%

Project Title

Design and Implementation of an Intelligent Fetal Health Status Diagnosis System Based on Machine Learning and Cardiotocography (CTG) Data Analysis

Given the critical importance of fetal health monitoring during pregnancy and the key role of early disorder detection, the development of intelligent systems based on artificial intelligence can create a major transformation in the field of maternal and fetal medicine.

DIAGNOSIS LEVELS

Normal Suspect Pathological

Current Challenges in Fetal Medicine

Heavy dependence on human diagnosis

Risk of physician error in CTG interpretation

Time-consuming data analysis

Lack of intelligent assistive tools in many medical centers

Project Benefits

Reduced diagnostic error

Increased analysis speed

Assists physician decisions

Usable in underserved areas

Foundation for intelligent medical systems

Project Objectives

Main Objective:

Design an intelligent system for fetal health status diagnosis using machine learning

Secondary Objectives:

Create high-accuracy ML model
Build fetal health diagnosis API
Design web-based dashboard for physicians
Create diagnosis results database
Enable real-time diagnosis system development

Dataset Specifications

Samples

2,126

records

Features

21

parameters

Source: Kaggle CTG Dataset

Data Type: Numerical & Statistical

Sample Features:

Fetal Heart Rate Accelerations Uterine Contractions Decelerations Variability Metrics

Phase 1

Data Preprocessing

Phase 2

Feature Extraction

Phase 3

ML Training

Phase 4

Model Evaluation

Phase 5

System Development

Machine Learning Models

ENSEMBLE • DEEP LEARNING

Random Forest

Ensemble

XGBoost

Gradient Boosting

SVM

Kernel

Neural Networks

Deep Learning

Accuracy

93.19%

Precision

94.2%

Recall

92.8%

F1 Score

93.5%

Project Innovations

Use of real medical data

Fast and automatic diagnosis capability

Expandable to hospital systems

Integration with CTG equipment

Foundation for AI-based medicine

Applications

Hospitals

Obstetrics & Gynecology Clinics

Medical Research Centers

Telemedicine Systems

Project Timeline

Data Analysis 1 month

ML Modeling 2 months

Software Development 2 months

Testing & Optimization 1 month

Total Project Duration: 6 months

System Architecture

Backend: Flask API

Database: SQLite/PostgreSQL

Frontend: Web Dashboard

API: Health Diagnosis Endpoint

SCIENTIFIC OUTPUTS

• Intelligent Fetal Health Diagnosis Model

• Data Analysis Report

• Optimized Diagnostic Algorithm

Conclusion

This project aims to develop an intelligent medical system, representing a significant step towards digitizing healthcare services and increasing medical diagnostic accuracy. Its successful implementation can pave the way for developing advanced systems in AI-based medicine.