2018英特爾ISEF獲獎作品摘要 系統(tǒng)軟件 Intel Finalist Abstract Systems Software

獲獎作品基本信息

年份	2018
學科	系統(tǒng)軟件 Systems Software
國家/州	United State of America

獲獎作品名稱

A Novel Machine Learning Approach for Determining the Confounding Factors for Cancer Identification: An Integration of Neural Learning and Decision Tree

獲獎作品摘要

Cancer is a heterogeneous disease comprising different subtypes. Its occurrence alters clinical parameters in human body. The exact cause being unknown, various internal factors, demographic and lifestyle related issues are found responsible. Identification of a set of confounding factors of cancer can be of immense help for early diagnosis.

In this project, we try to automatically identify the major factors causing cancer using machine learning. However, paucity of cancerous samples is the chief bottleneck. We design a novel neural network and decision tree based machine learning algorithm that can identify the set of main causal factors of cancer yielding more than 95% detection accuracy. This helps predict occurrence of cancer in patients.

Initially a generative restricted Boltzmann Machine (RBM), a supervised deep neural network, is used to model the bigger (non-cancerous) class. Functioning as one-class classifier, it confidently determines the non-cancerous samples (using the principle of free energy) to be removed from the dataset.? We also devise a heuristic based on inter-intra class distance ratio to prune noisy samples. The resultant near-balanced set is used to find out the causal factors using decision tree. The built-in tree is later used for cancer detection.? A multilayer perceptron based similar system is also designed.

Experiments are carried out with some benchmark datasets. The system is compared with existing Tomek link based pruning and 1-NN classifier for handling imbalanced data. An average increase in accuracy of 5%-50% is noticed. Moreover, the identified factors corroborate to medical literature. To further elicit the robustness of the system, experiments are conducted on datasets of other diseases and the enhancement is 5%-25%.

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高中生科研 英特爾 Intel ISEF
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高中生科研學術活動 英特爾 Intel ISEF 簡介

英特爾國際科學與工程大獎賽，簡稱 "ISEF"，由美國 Society for Science and the Public（科學和公共服務協(xié)會）主辦，英特爾公司冠名贊助，是全球規(guī)模最大、等級最高的中學生的科研科創(chuàng)賽事。ISEF 的學術活動學科包括了所有數(shù)學、自然科學、工程的全部領域和部分社會科學。ISEF 素有全球青少年科學學術活動的“世界杯”之美譽，旨在鼓勵學生團隊協(xié)作，開拓創(chuàng)新，長期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學術活動詳細介紹

英特爾 ISEF 全程指導方案

· 數(shù)學 · 物理 · 化學 · 生物 · 計算機 · 工程 ·

學科簡介：系統(tǒng)軟件 Systems Software

The study or development of software, information processes or methodologies to demonstrate, analyze, or control a process/solution.

Subcategories:

Algorithms?(ALG):?The study or creation of algorithms - step-by-step procedure of calculations to complete a specific task in data processing, automated reasoning and computing.

Cybersecurity?(CYB):?Studies involving the protection of a computer or computer system against unauthorized access or attacks. This can include studies involving hardware, network, software, host or multimedia security.

Databases?(DAT):?Studies that create or analyze data organization for ease of access, management and update.

Human/Machine Interface (HMC):?Software application that presents information to a user about the state of a process and to accept and implement the operator’s control instructions.

Languages and Operating Systems?(LNG):?Studies that involve the development or analysis of artificial languages used to write instructions that can be translated into machine language and then executed by a computer or system software responsible for the direct control and management of hardware and basic system operations of a computer or mobile device.

Mobile Apps?(APP):?A study involving a software application developed specifically for use on small, wireless computing devices. ?These studies may include front-end development techniques, such as user interface design and cross-platform support, and/or back-end development techniques, such as data services and business logic.

Online Learning (LRN):?Studies that focus on utilizing electronic technologies to access educational curriculum outside of a traditional means. ?Studies explore the design of learning activities and programs with online technologies, as well as the effective use of e-learning systems.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories.?If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.