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首页> 外文会议>International Conference on Data Management, Analytics and Innovation >Machine Learning to Diagnose Common Diseases Based on Symptoms
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Machine Learning to Diagnose Common Diseases Based on Symptoms

机译：机器学习基于症状诊断常见疾病

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It is often difficult for some people in rural India to diagnose a disease at an early stage due to lack of healthcare facilities and knowledge about its symptoms. Early diagnosis is the key for effective treatment of a disease and better living of the people. Providing sophisticated and accurate algorithms and techniques to overcome this issue will be revolutionary. Though hospitals in urban areas are using advanced technology for diagnosis and prognosis, proper diagnosis and prediction of diseases are a very hypercritical task. With the advancement of information technology and data sciences, disease diagnostic tools can be introduced to the general public, which can lower the burden of often depending on more expensive medical diagnostic technologies. Machine learning enables a system to learn from the previous data and takes decisions of its own. The programmer uses statistical techniques and feeds programs to the system using which it can store data and make decisions based on previous knowledge. The performance of the program increases with more and more training data. In this paper, we present a machine learning technique using Decision Tree Algorithm (DTL) to interconnect the symptoms and rearrange them and retrieve the most probable diagnosis. This technique allows the system to self-learn without using programming. This paper presents a system for diagnosis of common diseases, i.e., diabetes and heart diseases; by entering the symptoms into the system. Root node entropies are calculated for these two diseases by using the information gain for all the symptoms associated with these two diseases. The symptom values were categorized into three different levels, i.e., Low, Normal and High.
机译：由于缺乏医疗保健设施和关于其症状的知识，在印度农村的某些人诊断疾病通常难以诊断疾病。早期诊断是有效治疗疾病和更好的人民生活的关键。提供复杂和准确的算法和技术来克服这个问题将是革命性的。虽然城市地区的医院正在采用先进技术进行诊断和预后，但适当的诊断和预测疾病是一个非常过度的任务。随着信息技术和数据科学的进步，可以将疾病诊断工具引入公众，这可以降低频繁的负担，这取决于更昂贵的医疗诊断技术。机器学习使系统能够从以前的数据中学习并获得自己的决定。程序员使用统计技术并将程序馈送到使用它可以存储数据的系统并根据先前的知识进行决策。该程序的性能随着越来越多的培训数据而增加。在本文中，我们使用决策树算法（DTL）介绍了一种机器学习技术，以互连症状并重新排列并检索最可能的诊断。这种技术允许系统在不使用编程的情况下自学。本文介绍了诊断常见疾病的系统，即糖尿病和心脏病;通过进入系统中的症状。通过使用与这两种疾病相关的所有症状的信息增益来计算根节点熵为这两种疾病计算。症状值分为三个不同的水平，即低，正常和高。

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