Authors: M. Lakshmi Srija , CH . Siva Teja, P . Yagna Deep, T. Nandhini, CH. Venkata Satya Narayana, Mr. N. V. Murali Krishna Raja
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We address the critical need for early disease detection by focusing on the prediction of diseases based on user-provided symptoms using machine learning (ML). Our primary aim is to create a user-friendly and effective system for early disease detection using Random Forest Classifier. Users can input four symptoms, and behind the scenes, our ML model processes this information to make informed disease predictions. We\'ve leveraged a dataset containing records of 41 diseases and 132 Symptoms to develop and train our model, ensuring its accuracy. The project not only empowers individuals to take control of their health but also promises to enhance healthcare quality and reduce costs, benefiting both patients and the healthcare system. Our approach highlights the transformative potential of computer-assisted healthcare in saving lives and resources.
As healthcare becomes more intertwined with cutting-edge technology, the need for early disease detection takes center stage. Our project, however, takes a broader approach by focusing on the prediction of various diseases based on symptoms, offering a versatile solution to the evolving healthcare challenges. With the power of the Random Forest algorithm, we aim to empower individuals to take control of their health and make well-informed decisions by predicting a range of health-related diseases even before clinical signs manifest. In doing so, we contribute to proactive healthcare initiatives, ensuring individuals can embark on a path to better health with the aid of advanced technology and the foresight of machine learning.
II. LITERATURE SURVEY
Palle Pramod Reddy, Dirisinala Madhu Babu, Hardeep Kumar, and Dr. Shivi Sharma revolves around the development and application of a "Disease Prediction" method in healthcare. Their work centers on utilizing predictive modeling, specifically employing the random forest classifier, to estimate the likelihood of a user having a particular disease based on input symptoms. Through meticulous analysis, their method generates probabilities associated with various diseases, facilitating early detection and diagnosis. This innovative approach harnesses user-provided symptoms to enable a proactive healthcare model, aiming to enhance patient care through timely interventions and personalized disease management strategies.
Dr. C K Gomathy and Mr. A. Rohith Naidu at Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, Kanchipuram, introduces a Disease Prediction system employing machine learning methodologies to forecast diseases based on user-reported symptoms. Utilizing a Naïve Bayes classifier alongside other machine learning techniques like linear regression and decision trees, their system calculates the likelihood of specific diseases such as Diabetes, Malaria, Jaundice, Dengue, and Tuberculosis. By tapping into the wealth of biomedical and healthcare data, their innovative approach aims to enhance early disease detection, offering a valuable tool for improving the accuracy of diagnoses and streamlining treatment strategies, ultimately contributing to more efficient patient care and management.
Rinkal Keniya, Aman Khakharia, Vruddhi Shah, Vrushabh Gada, Ruchi Manjalkar, Tirth Thaker, Mahesh Warang, and Ninad Mehendale focuses on creating a robust medical diagnosis system using machine learning algorithms. With a focus on enhancing the accuracy and speed of disease prediction and diagnosis, the project addresses limitations in traditional diagnostic methods, particularly for severe illnesses. Their system incorporates multiple machine learning algorithms and utilizes a comprehensive dataset covering information on more than 230 diseases. By analyzing an individual's symptoms, age, and gender, the system offers predictions for potential diseases. Notably, the weighted KNN algorithm emerged as the most effective during testing, boasting an impressive prediction accuracy of 93.5%. This system serves as an early diagnostic tool, potentially playing a crucial role in timely treatment and interventions, thereby potentially saving lives through prompt medical care.
Kriti Gandhi, Mansi Mittal, Neha Gupta, and Shafali Dhall centers on integrating machine learning into healthcare practices, aiming to significantly enhance patient care standards. Acknowledging the critical need for advanced medical facilities, the project explores the application of various machine learning algorithms, including KNN, Random Forest, and Decision Tree Classifier, within healthcare procedures. Their primary objectives encompass improving patient satisfaction by optimizing treatment processes and highlighting the pivotal role of early disease prediction in healthcare. The project emphasizes the adage "Prevention is better than cure," emphasizing the importance of early detection in halting ailment progression. Of note, the project's focus on KNN and Logistic Regression signifies a specific emphasis on these machine learning techniques within the healthcare sector, underlining their potential for augmenting diagnostic and predictive capabilities in patient care scenarios.
Sneha Grampurohit and Chetan Sagarnal focuses on the application of data mining and machine learning in healthcare and biosciences. Their primary objective involves utilizing these technologies to extract crucial insights from medical data, specifically emphasizing the improvement of data analysis for early disease prediction and enhanced patient care. Through the utilization of machine learning, their project aims to create a system supporting healthcare professionals in early disease prediction and diagnosis. Their methodology involves working with a dataset containing records from 4,920 patients diagnosed with 41 different diseases. They meticulously curate 95 relevant independent variables (symptoms) from a pool of 132 for further analysis and optimization. Employing machine learning algorithms such as Decision Tree, Random Forest, and Naïve Bayes, their research conducts a comparative study to evaluate their effectiveness in disease prediction. The outcomes of this research contribute significantly to advancing healthcare services by enabling early disease detection and ultimately improving patient outcomes through more informed and timely interventions.
III. PROBLEM STATEMENT IN EXISTING SYSTEM
The healthcare sector confronts substantial obstacles when it comes to achieving early and precise disease diagnoses, rectifying diagnostic inaccuracies, handling intricate patient data, and coping with a shortage of medical expertise. There's a significant number of individuals grappling with diseases that could see more effective treatments and better outcomes if they were identified at an earlier stage. To tackle these challenges, the project sets out to build a disease prediction system utilizing machine learning. This system is designed to make accurate predictions about diseases based on symptoms reported by patients and their data. The primary objectives include managing data quality, safeguarding patient information, enhancing the transparency of the model's decision-making process, and building trust with patients. The ultimate goal is to substantially improve early disease detection and the overall management of healthcare, thereby reducing diagnostic inaccuracies and enabling healthcare professionals to provide more precise and prompt care to their patients.
A. Disadvantages in Existing System
IV. PROPOSED SYSTEM
A. Advantages In Proposed System
V. DATASET DESCRIPTION
The dataset for our project , "Disease Prediction Based on Symptoms Using Random Forest Algorithm" is a comprehensive collection encompassing 41 distinct diseases and 132 diverse symptoms. This dataset's richness reflects the complexity of real-world healthcare scenarios, enabling your machine learning model to predict a wide spectrum of health conditions, from common ailments to rarer diseases. Data quality and integrity are emphasized to ensure reliable predictions, and the dataset serves a dual purpose for both model training and testing. Preprocessing may have been applied to handle missing data and ensure data uniformity.
The methodology for our project follows a structured approach:
VII. SYSTEM ARCHITECTURE
The system architecture for your project on "Disease Prediction Based on Symptoms Using Random Forest Classifier" can be likened to a decision tree. Much like a decision tree, this project employs a straightforward and interpretable machine learning model, the Random Forest Classifier, which is versatile for both classification and regression tasks. It operates by recursively dividing input data into subsets based on symptom values. The architecture consists of nodes, representing decision points; branches, representing potential outcomes; and leaves, representing final predictions. Decision trees, in this context, partition the data based on symptom values to create nodes. The goal is to make splits that best classify the data into disease categories. This process continues until stopping criteria are met. In the case of classification, a leaf node typically represents a predicted disease label. The final prediction in your project is derived by aggregating the predictions of individual decision trees, often accomplished through techniques like majority voting. This architecture ensures an understandable and effective system for disease prediction based on user-reported symptoms using the Random Forest Classifier.
Our project on \"Disease Prediction Based on Symptoms Using Random Forest Classifier\" has successfully developed a user-friendly and accurate system for early disease detection. Leveraging machine learning and a comprehensive dataset, we\'ve created a valuable tool that can significantly improve healthcare by empowering individuals and healthcare providers. While challenges and limitations exist, our project represents a crucial step towards more efficient and effective disease prediction, ultimately contributing to better patient outcomes and healthcare quality. This work highlights the potential of technology to make a positive impact on public health and healthcare services.
 Palle Pramod Reddy, Dirisinala Madhu Babu, Hardeep Kumar and Dr.Shivi Sharma ,“Disease Prediction using Machine Learning” in 2021 , DOI:10.2139/ssrn.3167431.  Dr C K Gomathy, Mr. A. Rohith Naidu Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya , Kanchipuram “The prediction of disease using machine learning” in 2021,International Journal of Scientific Research in Engineering and Management (IJSREM), SSN: 2582-3930  Rinkal Keniya , Aman Khakharia ,Vruddhi Shah , Vrushabh Gada ,Ruchi Manjalkar , Tirth Thaker , Mahesh Warang , Ninad Mehendale,”Disease Prediction From Various Symptoms Using Machine Learning” in 2020,SocialScienceResearchNetwork,DOI:10.2139/ssrn.3661426  Kriti Gandhi, Mansi Mittal, Neha Gupta, Shafali Dhall ;” Disease Prediction using Machine Learning” in 2020, International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653  Sneha Grampurohit , Chetan Sagarnal, “Disease Prediction using Machine Learning Algorithms” in 2020 ,DOI:10.1109/INCET49848.2020.9154130
Copyright © 2023 M. Lakshmi Srija , CH . Siva Teja, P . Yagna Deep, T. Nandhini, CH. Venkata Satya Narayana, Mr. N. V. Murali Krishna Raja. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.