Smart AI PCOD/PCOS Detection System

• Author(s): Anjali B S Naik; Bindu D R; Ankitha A; Archana G; Anushree V; Dr. J. Narendra Babu ; Karthik N S
• Paper ID: 1719237
• Page: 3700-3705
• Published Date: 07-07-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 12 June-2026

Abstract

Polycystic Ovary Syndrome (PCOS) and Polycystic Ovarian Disease (PCOD) are one of the most common hormonal disorders in women in the world. It causes irregular menstrual cycle, infertility, metabolic disorder, insulin resistance, obesity and psychological stress. Major challenges for early detection and continuous monitoring are still limited awareness, delayed diagnosis and unaffordable personalized healthcare systems. This paper presents a Smart AI PCOD/PCOS Detection System, a web-based health care platform that uses integrated AI and IoT to help in early symptom analysis, menstrual cycle tracking, lifestyle monitoring and real-time physiological parameter analysis. The system incorporates two levels of diagnostics. Level 1 provides AI-assisted analysis of the menstrual and hormonal symptoms via a responsive web application. The users perform medically relevant questionnaire-based assessments related to menstrual irregularities, PMS symptoms, weight fluctuations, acne, hirsutism, bloating, sleep irregularities, and ovulation abnormalities. The gathered data is processed using a Machine learning prediction pipeline with a Random Forest classifier trained on real PCOS datasets to predict the risk probability. The level 2 is IoT based physiological monitoring, which includes temperature sensors, heartbeat sensors, insulin detection module, microcontroller unit and LCD display system for acquiring real-time biological readings. The sensor data is transmitted wirelessly to a backend server based on Flask and synchronized to Firebase Real-time Database. The system is equipped with Responsive Dashboards, Live Analytics using Chart.js, Professional PDF Medical Report Generation using Report Lab, Notification Systems, Multi-Language Support, AI Chatbot Assistance and Wellness Recommendation modules for Diet, Yoga, Hydration and Sleep Tracking. The collected data is processed through a Machine Learning prediction pipeline where a Random Forest classifier trained on real PCOS datasets is used to predict the risk probability. The second level entails IoT-based biological monitoring with the use of temperature sensors, heartbeat sensors, insulin detector, microcontroller units, and LCD screen displays for gathering real-time information. The sensor data is relayed via wireless means to the server built on Flask architecture and then synchronized through Firebase Real-time Database. There are also dashboards with live analytics based on Chart.js, professional PDF reports using Report Lab, notifications, multiple language support, AI chatbots, and health recommendation services such as dietary, yoga, hydration, and sleep management services. The mentioned platform will be evidence of the implementation of Artificial Intelligence, IoT, Machine Learning, web technology, and healthcare data analysis to create a menstruation health platform that will enhance the PCOD/PCOS early warning and healthcare monitoring system.

Keywords

PCOD, PCOS, Machine Learning, IoT, Flask, Firebase, Women’s Healthcare, Menstrual Tracking, Random Forest, AI Healthcare System, Ovulation Prediction, Real-Time Monitoring., Polycystic Ovary Syndrome, Polycystic Ovarian Disease, Irregular Menstrual Cycle, Infertility, Integrated AI, IoT, Web Application, PMS Symptoms, Hirsutism, Random Forest Classifier, IoT Based Physiological Monitoring, Firebase Real-Time Database, Temperature Sensors, Heartbeat Sensors, Insulin Detector, Microcontroller Units, LCD Screen Displays

Citations

IRE Journals:
Anjali B S Naik, Bindu D R, Ankitha A, Archana G; Anushree V, Dr. J. Narendra Babu ; Karthik N S "Smart AI PCOD/PCOS Detection System" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 3700-3705

IEEE:
Anjali B S Naik, Bindu D R, Ankitha A, Archana G; Anushree V, Dr. J. Narendra Babu ; Karthik N S "Smart AI PCOD/PCOS Detection System" Iconic Research And Engineering Journals, vol. 9, no. 12, Jun. 2026

APA:
Anjali B S Naik, Bindu D R, Ankitha A, Archana G; Anushree V, Dr. J. Narendra Babu ; Karthik N S (2026). Smart AI PCOD/PCOS Detection System. Iconic Research And Engineering Journals, 9(12).

MLA:
Anjali B S Naik, Bindu D R, Ankitha A, Archana G; Anushree V, Dr. J. Narendra Babu ; Karthik N S "Smart AI PCOD/PCOS Detection System" Iconic Research And Engineering Journals, vol. 9, no. 12, Jun. 2026.

BibTeX

@article{1719237,
author = {Anjali B S Naik, Bindu D R, Ankitha A, Archana G; Anushree V, Dr. J. Narendra Babu ; Karthik N S},
title = {Smart AI PCOD/PCOS Detection System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {3700-3705},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719237.pdf},
abstract = {Polycystic Ovary Syndrome (PCOS) and Polycystic Ovarian Disease (PCOD) are one of the most common hormonal disorders in women in the world. It causes irregular menstrual cycle, infertility, metabolic disorder, insulin resistance, obesity and psychological stress. Major challenges for early detection and continuous monitoring are still limited awareness, delayed diagnosis and unaffordable personalized healthcare systems. This paper presents a Smart AI PCOD/PCOS Detection System, a web-based health care platform that uses integrated AI and IoT to help in early symptom analysis, menstrual cycle tracking, lifestyle monitoring and real-time physiological parameter analysis. The system incorporates two levels of diagnostics. Level 1 provides AI-assisted analysis of the menstrual and hormonal symptoms via a responsive web application. The users perform medically relevant questionnaire-based assessments related to menstrual irregularities, PMS symptoms, weight fluctuations, acne, hirsutism, bloating, sleep irregularities, and ovulation abnormalities. The gathered data is processed using a Machine learning prediction pipeline with a Random Forest classifier trained on real PCOS datasets to predict the risk probability. The level 2 is IoT based physiological monitoring, which includes temperature sensors, heartbeat sensors, insulin detection module, microcontroller unit and LCD display system for acquiring real-time biological readings. The sensor data is transmitted wirelessly to a backend server based on Flask and synchronized to Firebase Real-time Database. The system is equipped with Responsive Dashboards, Live Analytics using Chart.js, Professional PDF Medical Report Generation using Report Lab, Notification Systems, Multi-Language Support, AI Chatbot Assistance and Wellness Recommendation modules for Diet, Yoga, Hydration and Sleep Tracking. The collected data is processed through a Machine Learning prediction pipeline where a Random Forest classifier trained on real PCOS datasets is used to predict the risk probability. The second level entails IoT-based biological monitoring with the use of temperature sensors, heartbeat sensors, insulin detector, microcontroller units, and LCD screen displays for gathering real-time information. The sensor data is relayed via wireless means to the server built on Flask architecture and then synchronized through Firebase Real-time Database. There are also dashboards with live analytics based on Chart.js, professional PDF reports using Report Lab, notifications, multiple language support, AI chatbots, and health recommendation services such as dietary, yoga, hydration, and sleep management services. The mentioned platform will be evidence of the implementation of Artificial Intelligence, IoT, Machine Learning, web technology, and healthcare data analysis to create a menstruation health platform that will enhance the PCOD/PCOS early warning and healthcare monitoring system.},
keywords = {PCOD, PCOS, Machine Learning, IoT, Flask, Firebase, Women’s Healthcare, Menstrual Tracking, Random Forest, AI Healthcare System, Ovulation Prediction, Real-Time Monitoring., Polycystic Ovary Syndrome, Polycystic Ovarian Disease, Irregular Menstrual Cycle, Infertility, Integrated AI, IoT, Web Application, PMS Symptoms, Hirsutism, Random Forest Classifier, IoT Based Physiological Monitoring, Firebase Real-Time Database, Temperature Sensors, Heartbeat Sensors, Insulin Detector, Microcontroller Units, LCD Screen Displays},
month = {June}
}